diff --git a/DESCRIPTION b/DESCRIPTION
index d28b74f..748cb70 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -37,7 +37,6 @@ Imports:
     dplyr (>= 1.1.0),
     glue (>= 1.5.0),
     omopgenerics (>= 0.1.2),
-    lifecycle,
     lubridate (>= 1.0.0),
     magrittr (>= 2.0.0),
     PatientProfiles (>= 1.1.0),
diff --git a/NAMESPACE b/NAMESPACE
index ad48845..48462d0 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -23,6 +23,8 @@ export(plotIncidence)
 export(plotPrevalence)
 export(settings)
 export(suppress)
+export(tableIncidence)
+export(tablePrevalence)
 importFrom(magrittr,"%>%")
 importFrom(omopgenerics,attrition)
 importFrom(omopgenerics,bind)
diff --git a/R/estimateIncidence.R b/R/estimateIncidence.R
index f0a912a..309e773 100644
--- a/R/estimateIncidence.R
+++ b/R/estimateIncidence.R
@@ -83,7 +83,7 @@ estimateIncidence <- function(cdm,
                               strata = list(),
                               includeOverallStrata = TRUE) {
 
-  summarisedResult <- FALSE
+  summarisedResult <- TRUE
 
   startCollect <- Sys.time()
 
@@ -401,6 +401,9 @@ estimateIncidence <- function(cdm,
       irs <- irs |>
         visOmopResults::uniteStrata()
     }
+    if(nrow(irs) == 0){
+      irs <- omopgenerics::emptySummarisedResult()
+    } else {
     irs <- irs |>
       dplyr::distinct() |>
       dplyr::mutate("analysis_id" = as.integer(.data$analysis_id)) |>
@@ -439,6 +442,7 @@ estimateIncidence <- function(cdm,
         "strata_name" = dplyr::if_else(.data$strata_name == "Overall", "overall", gsub(" and ", " &&& ", .data$strata_name)),
         "strata_level" = dplyr::if_else(.data$strata_level == "Overall", "overall", gsub(" and ", " &&& ", .data$strata_level))
       )
+    }
 
     irs <- omopgenerics::newSummarisedResult(irs, settings = analysisSettings) |>
       omopgenerics::suppress(minCellCount = minCellCount)
diff --git a/R/estimatePrevalence.R b/R/estimatePrevalence.R
index b79030f..7aeb993 100644
--- a/R/estimatePrevalence.R
+++ b/R/estimatePrevalence.R
@@ -193,7 +193,7 @@ estimatePrevalence <- function(cdm,
                                includeOverallStrata = TRUE,
                                minCellCount = 5) {
 
-  summarisedResult <- FALSE
+  summarisedResult <- TRUE
 
   startCollect <- Sys.time()
 
@@ -449,6 +449,10 @@ estimatePrevalence <- function(cdm,
       prs <- prs |>
         visOmopResults::uniteStrata()
     }
+
+    if(nrow(prs) == 0){
+      prs <- omopgenerics::emptySummarisedResult()
+    } else {
     prs <- prs |>
       dplyr::distinct() |>
       dplyr::mutate("analysis_id" = as.integer(.data$analysis_id)) |>
@@ -486,6 +490,7 @@ estimatePrevalence <- function(cdm,
         "strata_name" = dplyr::if_else(.data$strata_name == "Overall", "overall", gsub(" and ", " &&& ", .data$strata_name)),
         "strata_level" = dplyr::if_else(.data$strata_level == "Overall", "overall", gsub(" and ", " &&& ", .data$strata_level))
       )
+    }
 
     prs <- omopgenerics::newSummarisedResult(prs, settings = analysisSettings) |>
       omopgenerics::suppress(minCellCount = minCellCount)
diff --git a/R/tables.R b/R/tables.R
index 726cf11..806bc72 100644
--- a/R/tables.R
+++ b/R/tables.R
@@ -14,75 +14,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-#' Format a point_prevalence object into a visual table.
+
+
+#' Table of prevalence results
 #'
-#' `r lifecycle::badge("experimental")`
+#' @param result Prevalence results
+#' @param type Type of table. Can be "gt", "flextable", or "tibble".
+#' @param .options Table options to apply
 #'
-#' @param result A summarised_result object with results from
-#' estimatePointPrevalence() or estimatePeriodPrevalence().
-#' @param prevalenceType Type of prevalence estimates: "point" or "period".
-#' @param formatEstimateName Named list of estimate name's to join, sorted by
-#' computation order. Indicate estimate_name's between <...>.
-#' @param header A vector containing which elements should go into the header
-#' in order. Allowed are: `cdm_name`, `group`, `strata`, `additional`,
-#' `variable`, `estimate`, `settings`.
-#' @param splitStrata If TRUE strata columns will be splitted.
-#' @param cdmName If TRUE database names will be displayed.
-#' @param outcomeName If TRUE outcome cohort names will be displayed.
-#' @param outcomeSettings If TRUE settings related to the outcome cohorts will
-#' be displayed.
-#' @param denominatorName If TRUE denominator cohort names will be displayed.
-#' @param denominatorSettings If TRUE settings related to the denominator cohorts
-#' will be displayed.
-#' @param analysisSettings If TRUE database names will be displayed.
-#' @param groupColumn Column to use as group labels.
-#' @param type Type of desired formatted table, possibilities: "gt",
-#' "flextable", "tibble".
-#' @param .options Named list with additional formatting options.
-#' IncidencePrevalence::optionsTablePrevalence() shows allowed
-#' arguments and their default values.
+#' @return Table of prevalence results
+#' @export
 #'
 #' @examples
-#' \donttest{
-#' library(IncidencePrevalence)
-#'
-#' cdm <- mockIncidencePrevalenceRef()
-#'
-#' cdm <- generateDenominatorCohortSet(cdm = cdm, name = "denominator")
-#'
-#' prev <- estimatePointPrevalence(
-#'   cdm = cdm,
-#'   denominatorTable = "denominator",
-#'   outcomeTable = "outcome",
-#'   summarisedResult = TRUE
-#' )
-#'
-#' tablePrevalence(prev, prevalenceType = "point")
-#'
-#' CDMConnector::cdmDisconnect(cdm = cdm)
-#' }
-#'
-#' @return A table with a formatted version of a prevalence result.
-#'
-#' @noRd
-#'
 tablePrevalence <- function(
     result,
-    prevalenceType,
-    formatEstimateName = c(
-      "Denominator (N)" = "<denominator_count>",
-      "Outcome (N)" = "<outcome_count>",
-      "Prevalence [95% CI]" = "<prevalence> (<prevalence_95CI_lower> - <prevalence_95CI_upper>)"
-    ),
-    header = c("variable", "estimate"),
-    splitStrata = TRUE,
-    cdmName = TRUE,
-    outcomeName = TRUE,
-    outcomeSettings = FALSE,
-    denominatorName = TRUE,
-    denominatorSettings = TRUE,
-    analysisSettings = FALSE,
-    groupColumn = NULL,
     type = "gt",
     .options = list()
 ) {
@@ -92,6 +37,21 @@ tablePrevalence <- function(
     cli::cli_abort(c("x" = "Table functionality only works with results in a summarised_result format.", "i" = "These can be obtained with the argument `summarisedResult` in estimatePeriodPrevalence() and estimatePointPrevalence()."))
   }
 
+  formatEstimateName <- c(
+    "Denominator (N)" = "<denominator_count>",
+    "Outcome (N)" = "<outcome_count>",
+    "Prevalence [95% CI]" = "<prevalence> (<prevalence_95CI_lower> - <prevalence_95CI_upper>)"
+  )
+  header <- c("variable", "estimate")
+  splitStrata <- TRUE
+  cdmName <- TRUE
+  outcomeName <- TRUE
+  outcomeSettings <- FALSE
+  denominatorName <- TRUE
+  denominatorSettings <- TRUE
+  analysisSettings <- FALSE
+  groupColumn <- NULL
+
   tableInternal(
     result = result,
     formatEstimateName = formatEstimateName,
@@ -105,81 +65,24 @@ tablePrevalence <- function(
     analysisSettings = analysisSettings,
     groupColumn = groupColumn,
     type = type,
-    resultType = paste0(prevalenceType, "_prevalence"),
+    resultType = c("point_prevalence", "period_prevalence"),
     .options = .options
   )
 
 }
 
-#' Format a point_prevalence object into a visual table.
-#'
-#' `r lifecycle::badge("experimental")`
-#'
-#' @param result A summarised_result object with results from
-#' estimateIncidence().
-#' @param formatEstimateName Named list of estimate name's to join, sorted by
-#' computation order. Indicate estimate_name's between <...>.
-#' @param header A vector containing which elements should go into the header
-#' in order. Allowed are: `cdm_name`, `group`, `strata`, `additional`,
-#' `variable`, `estimate`, `settings`.
-#' @param splitStrata If TRUE strata columns will be splitted.
-#' @param cdmName If TRUE database names will be displayed.
-#' @param outcomeName If TRUE outcome cohort names will be displayed.
-#' @param outcomeSettings If TRUE settings related to the outcome cohorts will
-#' be displayed.
-#' @param denominatorName If TRUE denominator cohort names will be displayed.
-#' @param denominatorSettings If TRUE settings related to the denominator cohorts
-#' will be displayed.
-#' @param analysisSettings If TRUE database names will be displayed.
-#' @param groupColumn Column to use as group labels.
-#' @param type Type of desired formatted table, possibilities: "gt",
-#' "flextable", "tibble".
-#' @param .options Named list with additional formatting options.
-#' IncidencePrevalence::optionsTableIncidence() shows allowed
-#' arguments and their default values.
-#'
-#' @examples
-#' \donttest{
-#' library(IncidencePrevalence)
-#'
-#' cdm <- mockIncidencePrevalenceRef()
-#'
-#' cdm <- generateDenominatorCohortSet(cdm = cdm, name = "denominator")
-#'
-#' inc <- estimateIncidence(
-#'   cdm = cdm,
-#'   denominatorTable = "denominator",
-#'   outcomeTable = "outcome",
-#'   summarisedResult = TRUE
-#' )
-#'
-#' tableIncidence(inc)
+
+#' Table of incidence results
 #'
-#' CDMConnector::cdmDisconnect(cdm = cdm)
-#' }
+#' @param result Incidence results
+#' @param type Type of table. Can be "gt", "flextable", or "tibble".
+#' @param .options Table options to apply
 #'
-#' @return A table with a formatted version of incidence results.
+#' @return Table of results
+#' @export
 #'
-#' @noRd
 tableIncidence <- function(
     result,
-    formatEstimateName = c(
-      "Denominator (N)" = "<denominator_count>",
-      "Person-years" = "<person_years>",
-      "Outcome (N)" = "<outcome_count>",
-      "Incidence 100 person-years [95% CI]" =
-        "<incidence_100000_pys> (<incidence_100000_pys_95CI_lower> -
-      <incidence_100000_pys_95CI_upper>)"
-    ),
-    header = c("variable", "estimate"),
-    splitStrata = TRUE,
-    cdmName = TRUE,
-    outcomeName = TRUE,
-    outcomeSettings = FALSE,
-    denominatorName = TRUE,
-    denominatorSettings = TRUE,
-    analysisSettings = FALSE,
-    groupColumn = NULL,
     type = "gt",
     .options = list()
 ) {
@@ -189,9 +92,26 @@ tableIncidence <- function(
     cli::cli_abort(c("x" = "Table functionality only works with results in a summarised_result format.", "i" = "These can be obtained with the argument `summarisedResult` in estimateIncidence()."))
   }
 
+  header <- c("variable", "estimate")
+  splitStrata <- TRUE
+  cdmName <- TRUE
+  outcomeName <- TRUE
+  outcomeSettings <- FALSE
+  denominatorName <- TRUE
+  denominatorSettings <- TRUE
+  analysisSettings <- FALSE
+  groupColumn <- NULL
+
   tableInternal(
     result = result,
-    formatEstimateName = formatEstimateName,
+    formatEstimateName = c(
+      "Denominator (N)" = "<denominator_count>",
+      "Person-years" = "<person_years>",
+      "Outcome (N)" = "<outcome_count>",
+      "Incidence 100 person-years [95% CI]" =
+        "<incidence_100000_pys> (<incidence_100000_pys_95CI_lower> -
+      <incidence_100000_pys_95CI_upper>)"
+    ),
     header = header,
     splitStrata = splitStrata,
     cdmName = cdmName,
@@ -230,7 +150,7 @@ tableInternal <- function(
     .options = list()
 ) {
   result <- omopgenerics::newSummarisedResult(result) |>
-    visOmopResults::filterSettings(.data$result_type == resultType)
+    visOmopResults::filterSettings(.data$result_type %in% resultType)
   if (nrow(result) == 0) {
     cli::cli_abort("No results of the type {resultType} were found in the summarised result provided.")
   }
@@ -332,7 +252,7 @@ defaultTableIncidencePrevalence <- function(.options, type) {
 
   defaults <- visOmopResults::optionsVisOmopTable()
 
-  if (type == "incidence") {
+  if ("incidence" %in% type) {
     defaults$keepNotFormatted = FALSE
   }
 
diff --git a/man/tableIncidence.Rd b/man/tableIncidence.Rd
new file mode 100644
index 0000000..ed27a90
--- /dev/null
+++ b/man/tableIncidence.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tables.R
+\name{tableIncidence}
+\alias{tableIncidence}
+\title{Table of incidence results}
+\usage{
+tableIncidence(result, type = "gt", .options = list())
+}
+\arguments{
+\item{result}{Incidence results}
+
+\item{type}{Type of table. Can be "gt", "flextable", or "tibble".}
+
+\item{.options}{Table options to apply}
+}
+\value{
+Table of results
+}
+\description{
+Table of incidence results
+}
diff --git a/man/tablePrevalence.Rd b/man/tablePrevalence.Rd
new file mode 100644
index 0000000..56afa32
--- /dev/null
+++ b/man/tablePrevalence.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tables.R
+\name{tablePrevalence}
+\alias{tablePrevalence}
+\title{Table of prevalence results}
+\usage{
+tablePrevalence(result, type = "gt", .options = list())
+}
+\arguments{
+\item{result}{Prevalence results}
+
+\item{type}{Type of table. Can be "gt", "flextable", or "tibble".}
+
+\item{.options}{Table options to apply}
+}
+\value{
+Table of prevalence results
+}
+\description{
+Table of prevalence results
+}
diff --git a/tests/testthat/test-estimateIncidence.R b/tests/testthat/test-estimateIncidence.R
index 425c616..37c84d9 100644
--- a/tests/testthat/test-estimateIncidence.R
+++ b/tests/testthat/test-estimateIncidence.R
@@ -10,77 +10,18 @@ test_that("mock db: check output format", {
     interval = "months"
   )
 
-  # check estimates tibble
-  expect_true(all(c(
-    "analysis_id",
-    "n_persons",
-    "person_days",
-    "person_years",
-    "n_events",
-    "incidence_100000_pys",
-    "incidence_100000_pys_95CI_lower",
-    "incidence_100000_pys_95CI_upper",
-    "incidence_start_date",
-    "incidence_end_date",
-    "cohort_obscured",
-    "result_obscured",
-    "analysis_outcome_washout",
-    "analysis_repeated_events",
-    "analysis_interval",
-    "analysis_complete_database_intervals",
-    "analysis_min_cell_count",
-    "outcome_cohort_id",
-    "outcome_cohort_name",
-    "denominator_cohort_id",
-    "denominator_age_group",
-    "denominator_sex",
-    "denominator_days_prior_observation",
-    "denominator_start_date",
-    "denominator_end_date",
-    "denominator_target_cohort_definition_id",
-    "denominator_target_cohort_name",
-    "cdm_name"
-  ) %in%
-    names(inc)))
-
-  expect_true(all(c(
-    "analysis_id", "number_records", "number_subjects",
-    "reason_id", "reason",
-    "excluded_records", "excluded_subjects",
-    "analysis_outcome_washout",
-    "analysis_repeated_events",
-    "analysis_interval",
-    "analysis_complete_database_intervals",
-    "analysis_min_cell_count",
-    "outcome_cohort_id",
-    "outcome_cohort_name",
-    "denominator_cohort_id",
-    "denominator_age_group",
-    "denominator_sex",
-    "denominator_days_prior_observation",
-    "denominator_start_date",
-    "denominator_end_date",
-    "denominator_target_cohort_definition_id",
-    "denominator_target_cohort_name",
-    "cdm_name"
-  ) %in%
-    names(attrition(inc))))
-
   my_settings <- settings(inc)
   expect_true(nrow(my_settings) > 0)
 
-
-
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   interval = "months",
-  #   summarisedResult = TRUE
-  # )
-  # expect_true("summarised_result" %in% class(inc))
-  # expect_equal(colnames(inc),
-  #              omopgenerics::resultColumns())
+  inc <- estimateIncidence(
+    cdm = cdm,
+    denominatorTable = "denominator",
+    outcomeTable = "outcome",
+    interval = "months"
+  )
+  expect_true("summarised_result" %in% class(inc))
+  expect_equal(colnames(inc),
+               omopgenerics::resultColumns())
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -194,7 +135,10 @@ test_that("mock db: check working example 2", {
                            minCellCount = 0,
                            completeDatabaseIntervals = FALSE
   )
-  expect_true(sum(inc$n_events) == 1)
+  expect_true(sum(as.numeric(inc |>
+    dplyr::filter(
+      estimate_name == "outcome_count") |>
+    dplyr::pull("estimate_value"))) == 1)
 
   inc <- estimateIncidence(cdm,
                            denominatorTable = "denominator",
@@ -204,7 +148,10 @@ test_that("mock db: check working example 2", {
                            minCellCount = 0,
                            completeDatabaseIntervals = FALSE
   )
-  expect_true(sum(inc$n_events) == 3)
+  expect_true(sum(as.numeric(inc |>
+                               dplyr::filter(
+                                 estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == 3)
 
   inc <- estimateIncidence(cdm,
                            denominatorTable = "denominator",
@@ -214,7 +161,10 @@ test_that("mock db: check working example 2", {
                            minCellCount = 0,
                            completeDatabaseIntervals = FALSE
   )
-  expect_true(sum(inc$n_events) == 2)
+  expect_true(sum(as.numeric(inc |>
+                               dplyr::filter(
+                                 estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == 2)
 
   # even if repeatedEvents = TRUE,
   # if outcomeWashout=NULL (all of history)
@@ -227,7 +177,10 @@ test_that("mock db: check working example 2", {
                            minCellCount = 0,
                            completeDatabaseIntervals = FALSE
   )
-  expect_true(sum(inc$n_events) == 1)
+  expect_true(sum(as.numeric(inc |>
+                               dplyr::filter(
+                                 estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == 1)
 
   inc <- estimateIncidence(cdm,
                            denominatorTable = "denominator",
@@ -238,7 +191,10 @@ test_that("mock db: check working example 2", {
                            interval = "weeks",
                            completeDatabaseIntervals = FALSE
   )
-  expect_true(sum(inc$n_events) == 1)
+  expect_true(sum(as.numeric(inc |>
+                               dplyr::filter(
+                                 estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == 1)
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -294,8 +250,8 @@ test_that("mock db: check study periods", {
   # we expect 12 months of which the last in december
   # the last month should also be included
   # as the person goes up to the last day of the month
-  expect_true(nrow(inc) == 12)
-
+  expect_true(nrow(inc |>
+    dplyr::filter(estimate_name == "outcome_count")) == 12)
 
   inc <- estimateIncidence(cdm,
                            denominatorTable = "denominator",
@@ -309,7 +265,8 @@ test_that("mock db: check study periods", {
 
   # now with completeDatabaseIntervals is TRUE
   # we expect 10 months of which the last in november
-  expect_true(nrow(inc) == 10)
+  expect_true(nrow(inc |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 10)
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -375,12 +332,18 @@ test_that("mock db: check overall", {
   # one person had the event before the study period
   # (but washout was 0 so was included)
   # one person had the event during the study period
-  expect_true(nrow(inc) == 1)
-  expect_true(inc$n_persons == 2)
-  expect_true(inc$incidence_start_date ==
-                as.Date("2007-01-01"))
-  expect_true(inc$incidence_end_date ==
-                as.Date("2010-02-05")) # date of first event
+  expect_true(nrow(inc |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 1)
+  expect_true(inc |>
+    dplyr::filter(estimate_name == "denominator_count") |>
+    dplyr::pull("estimate_value") == "2")
+
+  expect_true(all(inc |>
+    visOmopResults::splitAdditional() |>
+    dplyr::pull("incidence_start_date") == as.Date("2007-01-01")))
+  expect_true(all(inc |>
+                    visOmopResults::splitAdditional() |>
+                    dplyr::pull("incidence_end_date") == as.Date("2010-02-05"))) # date of first event
 
 
   inc <- estimateIncidence(cdm,
@@ -392,11 +355,14 @@ test_that("mock db: check overall", {
                            minCellCount = 0,
                            completeDatabaseIntervals = FALSE
   )
-  expect_true(nrow(inc) == 1)
-  expect_true(inc$incidence_start_date ==
-                as.Date("2007-01-01"))
-  expect_true(inc$incidence_end_date ==
-                as.Date("2011-06-15")) # date of end of obs
+  expect_true(nrow(inc |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 1)
+  expect_true(all(inc |>
+                    visOmopResults::splitAdditional() |>
+                    dplyr::pull("incidence_start_date") == as.Date("2007-01-01")))
+  expect_true(all(inc |>
+                    visOmopResults::splitAdditional() |>
+                    dplyr::pull("incidence_end_date") == as.Date("2011-06-15"))) # date of end of obs
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -452,27 +418,36 @@ test_that("mock db: check person days", {
   )
 
   # in 2019 we expect person 2 to contribute from 1st july to end of December
-  expect_true(inc$person_days[1] ==
-                as.numeric(difftime(
-                  as.Date("2019-12-31"),
-                  as.Date("2019-07-01")
-                )) + 1)
+  expect_true(inc |>
+  dplyr::filter(estimate_name == "person_days") |>
+  head(1) |>
+  dplyr::pull("estimate_value") ==
+  as.numeric(difftime(
+    as.Date("2019-12-31"),
+    as.Date("2019-07-01")
+  )) + 1)
 
   # in 2020 we expect person 2 to contribute all year
   # and person 1 from 1st January to end of December
-  expect_true(inc$person_days[2] ==
-                (as.numeric(difftime(
-                  as.Date("2020-12-31"),
-                  as.Date("2020-07-01")
-                )) + 1) +
-                (as.numeric(difftime(
-                  as.Date("2020-12-31"),
-                  as.Date("2020-01-01")
-                ) + 1)))
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "person_days") |>
+                dplyr::filter(dplyr::row_number() == 2) |>
+                dplyr::pull("estimate_value") ==
+  (as.numeric(difftime(
+    as.Date("2020-12-31"),
+    as.Date("2020-07-01")
+  )) + 1) +
+    (as.numeric(difftime(
+      as.Date("2020-12-31"),
+      as.Date("2020-01-01")
+    ) + 1)))
 
   # in 2021 we expect person 2 to contribute all year
   # and person 1 from 1st January up to 27th june (date of their outcome)
-  expect_true(inc$person_days[3] ==
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "person_days") |>
+                dplyr::filter(dplyr::row_number() == 3) |>
+                dplyr::pull("estimate_value") ==
                 (as.numeric(difftime(
                   as.Date("2021-12-31"),
                   as.Date("2021-01-01")
@@ -482,9 +457,13 @@ test_that("mock db: check person days", {
                   as.Date("2021-01-01")
                 ) + 1)))
 
+
   # in 2022 we expect person 2 to contribute all year
   # (person 1 is out- they have had an event)
-  expect_true(inc$person_days[4] ==
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "person_days") |>
+                dplyr::filter(dplyr::row_number() == 4) |>
+                dplyr::pull("estimate_value") ==
                 (as.numeric(difftime(
                   as.Date("2021-10-05"),
                   as.Date("2021-01-01")
@@ -548,9 +527,15 @@ test_that("mock db: check periods follow calendar dates", {
     minCellCount = 0,
     completeDatabaseIntervals = FALSE
   )
-  expect_true(inc$n_events[1] == 1)
-  expect_true(inc$n_events[2] == 3)
 
+  expect_true(inc |>
+    dplyr::filter(estimate_name == "outcome_count") |>
+    dplyr::filter(dplyr::row_number() == 1) |>
+    dplyr::pull("estimate_value") == "1")
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "outcome_count") |>
+                dplyr::filter(dplyr::row_number() == 2) |>
+                dplyr::pull("estimate_value") == "3")
 
   # startDate during a month (with month as interval)
   cdm <- generateDenominatorCohortSet(
@@ -568,9 +553,18 @@ test_that("mock db: check periods follow calendar dates", {
     minCellCount = 0,
     completeDatabaseIntervals = FALSE
   )
-  expect_true(inc$n_events[1] == 1)
-  expect_true(inc$n_events[2] == 1)
-  expect_true(inc$n_events[3] == 1)
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "outcome_count") |>
+                dplyr::filter(dplyr::row_number() == 1) |>
+                dplyr::pull("estimate_value") == "1")
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "outcome_count") |>
+                dplyr::filter(dplyr::row_number() == 2) |>
+                dplyr::pull("estimate_value") == "1")
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "outcome_count") |>
+                dplyr::filter(dplyr::row_number() == 3) |>
+                dplyr::pull("estimate_value") == "1")
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -627,7 +621,9 @@ test_that("mock db: check washout windows", {
                              minCellCount = 0
   )
   # expect all events if we have zero days washout
-  expect_true(sum(incW0$n_events) == 4)
+  expect_true(sum(as.numeric(incW0 |>
+                dplyr::filter(estimate_name == "outcome_count") |>
+                dplyr::pull("estimate_value"))) == 4)
 
   incW1 <- estimateIncidence(cdm,
                              denominatorTable = "denominator",
@@ -638,7 +634,9 @@ test_that("mock db: check washout windows", {
                              minCellCount = 0
   )
   # expect three events if we have one days washout
-  expect_true(sum(incW1$n_events) == 3)
+  expect_true(sum(as.numeric(incW1 |>
+                               dplyr::filter(estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == 3)
 
   incW2 <- estimateIncidence(cdm,
                              denominatorTable = "denominator",
@@ -649,7 +647,9 @@ test_that("mock db: check washout windows", {
                              minCellCount = 0
   )
   # expect two events if we have two days washout
-  expect_true(sum(incW2$n_events) == 2)
+  expect_true(sum(as.numeric(incW2 |>
+                               dplyr::filter(estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == 2)
 
   incW365 <- estimateIncidence(cdm,
                                denominatorTable = "denominator",
@@ -660,9 +660,11 @@ test_that("mock db: check washout windows", {
                                minCellCount = 0
   )
   # expect one event if we have 365 days washout
-  expect_true(sum(incW365$n_events) == 1)
+  expect_true(sum(as.numeric(incW365 |>
+                               dplyr::filter(estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == 1)
 
-  incNull <- estimateIncidence(cdm,
+  incInf <- estimateIncidence(cdm,
                                denominatorTable = "denominator",
                                outcomeTable = "outcome",
                                repeatedEvents = TRUE,
@@ -671,12 +673,18 @@ test_that("mock db: check washout windows", {
                                minCellCount = 0
   )
   # expect one event if we have NULL (all history washout)
-  expect_true(sum(incNull$n_events) == 1)
+  expect_true(sum(as.numeric(incInf |>
+                               dplyr::filter(estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == 1)
 
   # but, we will have move days when using the 365 day washout
   # as the person came back to contribute more time at risk
-  expect_true(sum(incNull$person_days) <
-                sum(incW365$person_days))
+  expect_true(sum(as.numeric(incInf |>
+                   dplyr::filter(estimate_name == "person_days") |>
+                   dplyr::pull("estimate_value"))) <
+  sum(as.numeric(incW365 |>
+                   dplyr::filter(estimate_name == "person_days") |>
+                   dplyr::pull("estimate_value"))))
 
   CDMConnector::cdm_disconnect(cdm)
 
@@ -779,7 +787,9 @@ test_that("mock db: check events overlapping with start of a period", {
     minCellCount = 0
   )
 
-  expect_true(all(inc$n_persons == 1))
+  expect_true(all(inc |>
+      dplyr::filter(estimate_name == "denominator_count") |>
+      dplyr::pull("estimate_value") == 1))
   CDMConnector::cdm_disconnect(cdm)
 
   # another example
@@ -829,7 +839,9 @@ test_that("mock db: check events overlapping with start of a period", {
     interval = c("Years"),
     minCellCount = 0
   )
-  expect_true(all(inc2$n_persons == 1))
+  expect_true(all(inc2 |>
+                    dplyr::filter(estimate_name == "denominator_count") |>
+                    dplyr::pull("estimate_value") == 1))
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -890,22 +902,34 @@ test_that("mock db: compare results from months and years", {
   )
 
   # consistent results for months and years
-  expect_true(sum(incMonths$n_events) ==
-                sum(incYears$n_events))
-  expect_equal(
-    sum(incMonths$person_days),
-    sum(incYears$person_days)
-  )
-  expect_equal(
-    sum(incMonths$person_years),
-    sum(incYears$person_years)
-  )
+  expect_true(sum(as.numeric(incMonths |>
+    dplyr::filter(estimate_name == "outcome_count") |>
+    dplyr::pull("estimate_value")))  ==
+  sum(as.numeric(incYears |>
+                   dplyr::filter(estimate_name == "outcome_count") |>
+                   dplyr::pull("estimate_value"))))
+
+  expect_true(sum(as.numeric(incMonths |>
+                               dplyr::filter(estimate_name == "person_days") |>
+                               dplyr::pull("estimate_value")))  ==
+                sum(as.numeric(incYears |>
+                                 dplyr::filter(estimate_name == "person_days") |>
+                                 dplyr::pull("estimate_value"))))
+
+  expect_equal(sum(as.numeric(incMonths |>
+                               dplyr::filter(estimate_name == "person_years") |>
+                               dplyr::pull("estimate_value"))),
+                sum(as.numeric(incYears |>
+                                 dplyr::filter(estimate_name == "person_years") |>
+                                 dplyr::pull("estimate_value"))))
+
   CDMConnector::cdm_disconnect(cdm)
 
 
   cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
   cdm <- generateDenominatorCohortSet(
-    cdm = cdm, name = "denominator", cohortDateRange = c(as.Date("2010-01-01"), as.Date("2011-12-31"))
+    cdm = cdm, name = "denominator",
+    cohortDateRange = c(as.Date("2010-01-01"), as.Date("2011-12-31"))
   )
 
   incWeeks <- estimateIncidence(
@@ -942,41 +966,45 @@ test_that("mock db: compare results from months and years", {
   )
 
   # consistent results for months and years
-  expect_true(sum(incWeeks$n_events) ==
-                sum(incYears$n_events))
-  expect_true(sum(incQuarters$n_events) ==
-                sum(incYears$n_events))
-  expect_true(sum(incMonths$n_events) ==
-                sum(incYears$n_events))
-
-  expect_equal(
-    sum(incWeeks$person_days),
-    sum(incYears$person_days)
-  )
-  expect_equal(
-    sum(incQuarters$person_days),
-    sum(incYears$person_days)
-  )
-  expect_equal(
-    sum(incMonths$person_days),
-    sum(incYears$person_days)
-  )
-
-  expect_equal(
-    sum(incWeeks$person_years),
-    sum(incYears$person_years)
-  )
-  expect_equal(
-    sum(incQuarters$person_years),
-    sum(incYears$person_years)
-  )
-  expect_equal(
-    sum(incMonths$person_years),
-    sum(incYears$person_years)
-  )
-
-
-  CDMConnector::cdm_disconnect(cdm)
+  expect_equal(sum(as.numeric(incWeeks |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                dplyr::pull("estimate_value"))),
+               sum(as.numeric(incYears |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                dplyr::pull("estimate_value"))))
+  expect_equal(sum(as.numeric(incQuarters |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                dplyr::pull("estimate_value"))),
+               sum(as.numeric(incYears |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                dplyr::pull("estimate_value"))))
+  expect_equal(sum(as.numeric(incMonths |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                dplyr::pull("estimate_value"))),
+               sum(as.numeric(incYears |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                dplyr::pull("estimate_value"))))
+
+  expect_equal(sum(as.numeric(incWeeks |>
+                                dplyr::filter(estimate_name == "person_days") |>
+                                dplyr::pull("estimate_value"))),
+               sum(as.numeric(incYears |>
+                                dplyr::filter(estimate_name == "person_days") |>
+                                dplyr::pull("estimate_value"))))
+  expect_equal(sum(as.numeric(incQuarters |>
+                                dplyr::filter(estimate_name == "person_days") |>
+                                dplyr::pull("estimate_value"))),
+               sum(as.numeric(incYears |>
+                                dplyr::filter(estimate_name == "person_days") |>
+                                dplyr::pull("estimate_value"))))
+  expect_equal(sum(as.numeric(incMonths |>
+                                dplyr::filter(estimate_name == "person_days") |>
+                                dplyr::pull("estimate_value"))),
+               sum(as.numeric(incYears |>
+                                dplyr::filter(estimate_name == "person_days") |>
+                                dplyr::pull("estimate_value"))))
+
+    CDMConnector::cdm_disconnect(cdm)
 })
 
 test_that("mock db: check entry and event on same day", {
@@ -1023,7 +1051,10 @@ test_that("mock db: check entry and event on same day", {
     minCellCount = 0,
     completeDatabaseIntervals = FALSE
   )
-  expect_true(sum(incWithoutRep$n_events) == 1)
+
+  expect_true(sum(as.numeric(incWithoutRep |>
+                   dplyr::filter(estimate_name == "outcome_count") |>
+                   dplyr::pull("estimate_value"))) == "1")
 
   incWithRep <- estimateIncidence(
     cdm = cdm,
@@ -1035,7 +1066,9 @@ test_that("mock db: check entry and event on same day", {
     minCellCount = 0,
     completeDatabaseIntervals = FALSE
   )
-  expect_true(sum(incWithRep$n_events) == 1)
+  expect_true(sum(as.numeric(incWithRep |>
+                               dplyr::filter(estimate_name == "outcome_count") |>
+                               dplyr::pull("estimate_value"))) == "1")
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -1089,8 +1122,9 @@ test_that("mock db: cohort start overlaps with the outcome", {
     interval = c("Years"),
     minCellCount = 0
   )
-
-  expect_true(all(inc$n_persons == 1L))
+  expect_true(all(inc |>
+    dplyr::filter(estimate_name == "denominator_count") |>
+    dplyr::pull("estimate_value") == "1"))
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -1147,7 +1181,9 @@ test_that("mock db: check outcome in previous obeservation period", {
     interval = c("Years"),
     minCellCount = 0
   )
-  expect_true(all(incRep$n_persons == 2))
+  expect_true(all(incRep |>
+    dplyr::filter(estimate_name == "denominator_count") |>
+    dplyr::pull("estimate_value") == "2"))
 
   # with inf wash out- should only have 1 person
   incNoRep <- estimateIncidence(
@@ -1159,7 +1195,9 @@ test_that("mock db: check outcome in previous obeservation period", {
     interval = c("Years"),
     minCellCount = 0
   )
-  expect_true(all(incNoRep$n_persons == 1))
+  expect_true(all(incNoRep |>
+                    dplyr::filter(estimate_name == "denominator_count") |>
+                    dplyr::pull("estimate_value") == "1"))
 
   # with 5 year wash out- should have 2 people at the start of the study period
   incNoRep2 <- estimateIncidence(
@@ -1171,8 +1209,10 @@ test_that("mock db: check outcome in previous obeservation period", {
     interval = c("Years"),
     minCellCount = 0
   )
-  expect_true(max(incNoRep2$n_persons) == 2)
 
+  expect_true(max(as.numeric(incNoRep2 |>
+                    dplyr::filter(estimate_name == "denominator_count") |>
+                    dplyr::pull("estimate_value"))) == 2)
 
   CDMConnector::cdm_disconnect(cdm)
 
@@ -1236,20 +1276,56 @@ test_that("mock db: check minimum counts", {
     minCellCount = 0,
     completeDatabaseIntervals = FALSE
   )
-  expect_true(inc$n_persons[1] == 20)
-  expect_true(inc$n_persons[2] == 3)
-  expect_true(!is.na(inc$person_days[1]))
-  expect_true(!is.na(inc$person_days[2]))
-  expect_true(!is.na(inc$person_years[1]))
-  expect_true(!is.na(inc$person_years[2]))
-  expect_true(inc$n_events[1] == 17)
-  expect_true(inc$n_events[2] == 3)
-  expect_true(!is.na(inc$incidence_100000_pys[1]))
-  expect_true(!is.na(inc$incidence_100000_pys[2]))
-  expect_true(!is.na(inc$incidence_100000_pys_95CI_lower[1]))
-  expect_true(!is.na(inc$incidence_100000_pys_95CI_lower[2]))
-  expect_true(!is.na(inc$incidence_100000_pys_95CI_upper[1]))
-  expect_true(!is.na(inc$incidence_100000_pys_95CI_upper[2]))
+
+
+  expect_equal(inc |>
+    dplyr::filter(estimate_name == "denominator_count") |>
+    dplyr::pull("estimate_value"), c("20", "3"))
+  expect_equal(inc |>
+                 dplyr::filter(estimate_name == "outcome_count") |>
+                 dplyr::pull("estimate_value"), c("17", "3"))
+  expect_true(!is.na(inc |>
+    dplyr::filter(estimate_name == "person_days") |>
+    dplyr::filter(dplyr::row_number() == 1) |>
+    dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "person_days") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "person_years") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "person_years") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys_95CI_lower") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys_95CI_lower") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys_95CI_upper") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys_95CI_upper") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+
 
   inc <- estimateIncidence(
     cdm = cdm,
@@ -1260,27 +1336,66 @@ test_that("mock db: check minimum counts", {
     minCellCount = 5,
     completeDatabaseIntervals = FALSE
   )
-  expect_true(inc$n_persons[1] == 20)
-  expect_true(is.na(inc$n_persons[2]))
-  expect_true(!is.na(inc$person_days[1]))
-  expect_true(is.na(inc$person_days[2]))
-  expect_true(!is.na(inc$person_years[1]))
-  expect_true(is.na(inc$person_years[2]))
-  expect_true(inc$n_events[1] == 17)
-  expect_true(is.na(inc$n_events[2]))
-  expect_true(!is.na(inc$incidence_100000_pys[1]))
-  expect_true(is.na(inc$incidence_100000_pys[2]))
-  expect_true(!is.na(inc$incidence_100000_pys_95CI_lower[1]))
-  expect_true(is.na(inc$incidence_100000_pys_95CI_lower[2]))
-  expect_true(!is.na(inc$incidence_100000_pys_95CI_upper[1]))
-  expect_true(is.na(inc$incidence_100000_pys_95CI_upper[2]))
+  expect_equal(inc |>
+                 dplyr::filter(estimate_name == "denominator_count") |>
+                 dplyr::pull("estimate_value"), c("20", NA))
+  expect_equal(inc |>
+                 dplyr::filter(estimate_name == "outcome_count") |>
+                 dplyr::pull("estimate_value"), c("17", NA))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "person_days") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(is.na(inc |>
+                       dplyr::filter(estimate_name == "person_days") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "person_years") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(is.na(inc |>
+                       dplyr::filter(estimate_name == "person_years") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys_95CI_lower") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys_95CI_lower") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(!is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys_95CI_upper") |>
+                       dplyr::filter(dplyr::row_number() == 1) |>
+                       dplyr::pull("estimate_value")))
+  expect_true(is.na(inc |>
+                       dplyr::filter(estimate_name == "incidence_100000_pys_95CI_upper") |>
+                       dplyr::filter(dplyr::row_number() == 2) |>
+                       dplyr::pull("estimate_value")))
+
+
 
   CDMConnector::cdm_disconnect(cdm)
 })
 
 test_that("mock db: multiple overlapping outcomes", {
+
+  # technically overlapping outcomes are not allowed
+  # check this edge case, but validation might also not allow this
+
   skip_on_cran()
-  # two
+  # two people
   personTable <- dplyr::tibble(
     person_id = c(1L, 2L),
     gender_concept_id = c(8507L, 8532L),
@@ -1300,6 +1415,7 @@ test_that("mock db: multiple overlapping outcomes", {
       as.Date("2021-12-31")
     )
   )
+  # two outcomes for person one
   outcomeTable <- dplyr::tibble(
     cohort_definition_id = c(1L, 1L),
     subject_id = c(1L, 1L),
@@ -1321,10 +1437,16 @@ test_that("mock db: multiple overlapping outcomes", {
     outcomeTable = "outcome",
     outcomeWashout = 180,
     repeatedEvents = TRUE,
-    interval = c("Years"),
+    interval = "overall",
     minCellCount = 0
   )
-  expect_true(all(inc$n_persons) == 1)
+
+  expect_true(inc |>
+    dplyr::filter(estimate_name == "denominator_count") |>
+    dplyr::pull("estimate_value") == "2")
+  expect_true(inc |>
+    dplyr::filter(estimate_name == "outcome_count") |>
+    dplyr::pull("estimate_value") == "1")
   CDMConnector::cdm_disconnect(cdm)
 
   # three
@@ -1376,10 +1498,15 @@ test_that("mock db: multiple overlapping outcomes", {
     outcomeTable = "outcome",
     outcomeWashout = 180,
     repeatedEvents = TRUE,
-    interval = c("Years"),
+    interval = "overall",
     minCellCount = 0
   )
-  expect_true(all(inc$n_persons) == 1)
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "denominator_count") |>
+                dplyr::pull("estimate_value") == "2")
+  expect_true(inc |>
+                dplyr::filter(estimate_name == "outcome_count") |>
+                dplyr::pull("estimate_value") == "1")
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -1443,7 +1570,9 @@ test_that("mock db: cohort before period start ending after period", {
     completeDatabaseIntervals = FALSE,
     minCellCount = 0
   )
-  expect_true(all(inc$n_events == 1L))
+  expect_true(all(inc |>
+                dplyr::filter(estimate_name == "outcome_count") |>
+                dplyr::pull("estimate_value") == "1"))
 
   # washout
   inc <- estimateIncidence(
@@ -1456,7 +1585,9 @@ test_that("mock db: cohort before period start ending after period", {
     completeDatabaseIntervals = FALSE,
     minCellCount = 0
   )
-  expect_true(all(inc$n_events == 1L))
+  expect_true(all(inc |>
+                    dplyr::filter(estimate_name == "outcome_count") |>
+                    dplyr::pull("estimate_value") == "1"))
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -1561,7 +1692,10 @@ test_that("mock db: check full period requirement - year", {
     interval = c("Years"),
     minCellCount = 0
   )
-  expect_true(nrow(inc) == 1)
+
+  expect_true(nrow(inc |>
+                    dplyr::filter(estimate_name == "outcome_count")) ==
+                1)
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -1666,7 +1800,6 @@ test_that("mock db: check full period requirement - month", {
     interval = c("Months"),
     minCellCount = 0
   )
-  expect_true(inc$n_persons == 1)
   expect_true(nrow(inc) >= 1)
 
   CDMConnector::cdm_disconnect(cdm)
@@ -1724,9 +1857,20 @@ test_that("mock db: check completeDatabaseIntervals", {
     completeDatabaseIntervals = TRUE,
     minCellCount = 0
   )
-  expect_true(nrow(inc) == 2)
-  expect_true(lubridate::year(inc$incidence_start_date[1]) == "2020")
-  expect_true(lubridate::year(inc$incidence_start_date[2]) == "2021")
+
+  expect_true(nrow(inc |>
+    dplyr::filter(estimate_name == "outcome_count")) == 2)
+  expect_true(lubridate::year(inc |>
+    dplyr::filter(estimate_name == "outcome_count") |>
+    visOmopResults::splitAdditional() |>
+    dplyr::filter(dplyr::row_number() == 1) |>
+    dplyr::pull("incidence_start_date")) == "2020")
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 2) |>
+                                dplyr::pull("incidence_start_date")) == "2021")
+
   # repetitive events FALSE
   # - now we expect only to use 2020 (id 2 obs end is in 21)
   inc <- estimateIncidence(
@@ -1738,8 +1882,13 @@ test_that("mock db: check completeDatabaseIntervals", {
     completeDatabaseIntervals = TRUE,
     minCellCount = 0
   )
-  expect_true(nrow(inc) == 1)
-  expect_true(lubridate::year(inc$incidence_start_date[1]) == "2020")
+  expect_true(nrow(inc |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 1)
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 1) |>
+                                dplyr::pull("incidence_start_date")) == "2020")
 
   # full periods required FALSE
   # repetitive events TRUE
@@ -1755,11 +1904,29 @@ test_that("mock db: check completeDatabaseIntervals", {
     completeDatabaseIntervals = FALSE,
     minCellCount = 0
   )
-  expect_true(nrow(inc) == 4)
-  expect_true(lubridate::year(inc$incidence_start_date[1]) == "2019")
-  expect_true(lubridate::year(inc$incidence_start_date[2]) == "2020")
-  expect_true(lubridate::year(inc$incidence_start_date[3]) == "2021")
-  expect_true(lubridate::year(inc$incidence_start_date[4]) == "2022")
+  expect_true(nrow(inc |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 4)
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 1) |>
+                                dplyr::pull("incidence_start_date")) == "2019")
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 2) |>
+                                dplyr::pull("incidence_start_date")) == "2020")
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 3) |>
+                                dplyr::pull("incidence_start_date")) == "2021")
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 4) |>
+                                dplyr::pull("incidence_start_date")) == "2022")
+
   # repetitive events FALSE
   inc <- estimateIncidence(
     cdm = cdm,
@@ -1770,10 +1937,23 @@ test_that("mock db: check completeDatabaseIntervals", {
     completeDatabaseIntervals = FALSE,
     minCellCount = 0
   )
-  expect_true(nrow(inc) == 3)
-  expect_true(lubridate::year(inc$incidence_start_date[1]) == "2019")
-  expect_true(lubridate::year(inc$incidence_start_date[2]) == "2020")
-  expect_true(lubridate::year(inc$incidence_start_date[3]) == "2021")
+  expect_true(nrow(inc |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 3)
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 1) |>
+                                dplyr::pull("incidence_start_date")) == "2019")
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 2) |>
+                                dplyr::pull("incidence_start_date")) == "2020")
+  expect_true(lubridate::year(inc |>
+                                dplyr::filter(estimate_name == "outcome_count") |>
+                                visOmopResults::splitAdditional() |>
+                                dplyr::filter(dplyr::row_number() == 3) |>
+                                dplyr::pull("incidence_start_date")) == "2021")
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -1941,30 +2121,38 @@ test_that("mock db: check with and without study start and end date", {
   )
 
   # given the settings above we would expect the same results for 2010
-  expect_true(inc1A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull() ==
-                inc2A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull())
-  expect_true(inc1A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull() ==
-                inc2A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull())
-  expect_true(inc1A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull() ==
-                inc2A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull())
+  expect_equal(inc1A |>
+    dplyr::filter(estimate_name == "denominator_count") |>
+    visOmopResults::splitAdditional() |>
+    dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+    dplyr::pull("estimate_value"),
+  inc2A |>
+    dplyr::filter(estimate_name == "denominator_count") |>
+    visOmopResults::splitAdditional() |>
+    dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+    dplyr::pull("estimate_value"))
+
+  expect_equal(inc1A |>
+                 dplyr::filter(estimate_name == "person_days") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"),
+               inc2A |>
+                 dplyr::filter(estimate_name == "person_days") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"))
+
+  expect_equal(inc1A |>
+                 dplyr::filter(estimate_name == "outcome_count") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"),
+               inc2A |>
+                 dplyr::filter(estimate_name == "outcome_count") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"))
 
   # 365 washout, repetitive events
   inc1B <- estimateIncidence(cdm,
@@ -1986,31 +2174,38 @@ test_that("mock db: check with and without study start and end date", {
                              completeDatabaseIntervals = FALSE
   )
   # given the settings above we would expect the same results for 2010
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull() ==
-                inc2B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull())
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull() ==
-                inc2B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull())
-
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull() ==
-                inc2B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull())
+  expect_equal(inc1B |>
+                 dplyr::filter(estimate_name == "denominator_count") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"),
+               inc2B |>
+                 dplyr::filter(estimate_name == "denominator_count") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"))
+
+  expect_equal(inc1B |>
+                 dplyr::filter(estimate_name == "person_days") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"),
+               inc2B |>
+                 dplyr::filter(estimate_name == "person_days") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"))
+
+  expect_equal(inc1B |>
+                 dplyr::filter(estimate_name == "outcome_count") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"),
+               inc2B |>
+                 dplyr::filter(estimate_name == "outcome_count") |>
+                 visOmopResults::splitAdditional() |>
+                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) |>
+                 dplyr::pull("estimate_value"))
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -2050,38 +2245,40 @@ test_that("mock db: check study start and end date 1000", {
                              completeDatabaseIntervals = FALSE
   )
 
+
+
+
+
   expect_true(inc1A %>%
+               visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull() ==
-                inc2A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull())
-  expect_true(inc1A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull() ==
+                dplyr::filter(estimate_name == "denominator_count") %>%
+                dplyr::pull("estimate_value") ==
                 inc2A %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull())
+                dplyr::filter(estimate_name == "denominator_count") %>%
+                dplyr::pull("estimate_value"))
   expect_true(inc1A %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull() ==
+                dplyr::filter(estimate_name == "person_days") %>%
+                dplyr::pull("estimate_value") ==
                 inc2A %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull())
+                dplyr::filter(estimate_name == "person_days") %>%
+                dplyr::pull("estimate_value"))
   expect_true(inc1A %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("incidence_100000_pys") %>%
-                dplyr::pull() ==
+                dplyr::filter(estimate_name == "outcome_count") %>%
+                dplyr::pull("estimate_value") ==
                 inc2A %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("incidence_100000_pys") %>%
-                dplyr::pull())
+                dplyr::filter(estimate_name == "outcome_count") %>%
+                dplyr::pull("estimate_value"))
 
   # 365 washout, repetitive events
   inc1B <- estimateIncidence(cdm,
@@ -2104,38 +2301,15 @@ test_that("mock db: check study start and end date 1000", {
   )
 
   expect_true(inc1B %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull() ==
-                inc2B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull())
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull() ==
+                dplyr::filter(estimate_name == "incidence_100000_pys") %>%
+                dplyr::pull("estimate_value") ==
                 inc2B %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull())
-
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull() ==
-                inc2B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull())
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("incidence_100000_pys") %>%
-                dplyr::pull() ==
-                inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("incidence_100000_pys") %>%
-                dplyr::pull())
+                dplyr::filter(estimate_name == "incidence_100000_pys") %>%
+                dplyr::pull("estimate_value"))
   CDMConnector::cdm_disconnect(cdm)
 
   # with multiple outcomes per person
@@ -2174,38 +2348,18 @@ test_that("mock db: check study start and end date 1000", {
                              completeDatabaseIntervals = FALSE
   )
 
+
   expect_true(inc1A %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull() ==
-                inc2A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull())
-  expect_true(inc1A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull() ==
-                inc2A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull())
-  expect_true(inc1A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull() ==
-                inc2A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull())
-  expect_true(inc1A %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("incidence_100000_pys") %>%
-                dplyr::pull() ==
+                dplyr::filter(estimate_name == "incidence_100000_pys") %>%
+                dplyr::pull("estimate_value") ==
                 inc2A %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("incidence_100000_pys") %>%
-                dplyr::pull())
+                dplyr::filter(estimate_name == "incidence_100000_pys") %>%
+                dplyr::pull("estimate_value"))
+
 
   # 365 washout, repetitive events
   inc1B <- estimateIncidence(cdm,
@@ -2227,38 +2381,17 @@ test_that("mock db: check study start and end date 1000", {
                              completeDatabaseIntervals = FALSE
   )
 
+
   expect_true(inc1B %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull() ==
-                inc2B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_persons") %>%
-                dplyr::pull())
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull() ==
-                inc2B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("person_days") %>%
-                dplyr::pull())
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull() ==
-                inc2B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("n_events") %>%
-                dplyr::pull())
-  expect_true(inc1B %>%
-                dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("incidence_100000_pys") %>%
-                dplyr::pull() ==
+                dplyr::filter(estimate_name == "incidence_100000_pys") %>%
+                dplyr::pull("estimate_value") ==
                 inc2B %>%
+                visOmopResults::splitAdditional() |>
                 dplyr::filter(lubridate::year(incidence_start_date) == 2010) %>%
-                dplyr::select("incidence_100000_pys") %>%
-                dplyr::pull())
+                dplyr::filter(estimate_name == "incidence_100000_pys") %>%
+                dplyr::pull("estimate_value"))
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -2422,7 +2555,9 @@ test_that("mock db: multiple observation periods", {
                              minCellCount = 0
   )
   # expect all events if we have zero days washout
-  expect_true(sum(incW0$n_events) == 2)
+  expect_true(sum(as.numeric(incW0 %>%
+    dplyr::filter(estimate_name == "outcome_count") %>%
+    dplyr::pull("estimate_value"))) == 2)
   CDMConnector::cdm_disconnect(cdm)
 
   # Change the inclusion so that both patients have valid observation periods. Now 1 should have two, and 2 one.
@@ -2464,7 +2599,9 @@ test_that("mock db: multiple observation periods", {
                               minCellCount = 0
   )
   # expect all events if we have ten days washout
-  expect_true(sum(incW10$n_events) == 3)
+  expect_true(sum(as.numeric(incW10 %>%
+                               dplyr::filter(estimate_name == "outcome_count") %>%
+                               dplyr::pull("estimate_value"))) == 3)
   CDMConnector::cdm_disconnect(cdm)
 
   # try event not counted for outcome but counted for washout as denominator (before observ period)
@@ -2507,8 +2644,17 @@ test_that("mock db: multiple observation periods", {
                                       completeDatabaseIntervals = FALSE,
                                       minCellCount = 0
   )
-  expect_true(sum(inc_PreWashout$n_events) == 3)
-  expect_true(inc_PreWashout %>% dplyr::select(person_days) %>% sum() == as.numeric(difftime(as.Date("2005-08-11"), as.Date("2005-07-19"))) + 1 - 2 + as.numeric(difftime(as.Date("2015-01-02"), as.Date("2009-04-10"))) + 1 - 10 + as.numeric(difftime(as.Date("2011-12-11"), as.Date("2010-12-11"))) + 1 - 10 - 3)
+  expect_true(sum(as.numeric(inc_PreWashout %>%
+                               dplyr::filter(estimate_name == "outcome_count") %>%
+                               dplyr::pull("estimate_value"))) == 3)
+  expect_true(sum(as.numeric(inc_PreWashout %>%
+                               dplyr::filter(estimate_name == "person_days") %>%
+                               dplyr::pull("estimate_value"))) ==
+                as.numeric(difftime(as.Date("2005-08-11"), as.Date("2005-07-19"))) +
+                1 - 2 + as.numeric(difftime(as.Date("2015-01-02"),
+                                            as.Date("2009-04-10"))) + 1 - 10 +
+                as.numeric(difftime(as.Date("2011-12-11"), as.Date("2010-12-11"))) +
+                1 - 10 - 3)
   CDMConnector::cdm_disconnect(cdm)
 
   # multiple events in one of the observation periods of person 1
@@ -2559,17 +2705,25 @@ test_that("mock db: multiple observation periods", {
   )
 
   # we should have 4 events with washout 0, but 3 events with washout 30
-  expect_true(sum(inc_Mult1_W0$n_events) == 4)
-  expect_true(sum(inc_Mult1_W30$n_events) == 3)
-  expect_true(inc_Mult1_W0 %>% dplyr::select(person_days) %>%
-                sum() == as.numeric(difftime(as.Date("2005-08-11"),
+  expect_true(sum(as.numeric(inc_Mult1_W0 %>%
+                               dplyr::filter(estimate_name == "outcome_count") %>%
+                               dplyr::pull("estimate_value"))) == 4)
+  expect_true(sum(as.numeric(inc_Mult1_W30 %>%
+                               dplyr::filter(estimate_name == "outcome_count") %>%
+                               dplyr::pull("estimate_value"))) == 3)
+  expect_true(sum(as.numeric(inc_Mult1_W0 %>%
+                               dplyr::filter(estimate_name == "person_days") %>%
+                               dplyr::pull("estimate_value"))) ==
+                as.numeric(difftime(as.Date("2005-08-11"),
                                              as.Date("2005-06-19"))) + 1 +
                 as.numeric(difftime(as.Date("2015-01-02"),
                                     as.Date("2009-04-10"))) + 1 +
                 as.numeric(difftime(as.Date("2011-12-11"),
                                     as.Date("2010-12-11"))) + 1)
-  expect_true(inc_Mult1_W30 %>% dplyr::select(person_days) %>%
-                sum() == as.numeric(difftime(as.Date("2005-08-11"),
+  expect_true(sum(as.numeric(inc_Mult1_W30 %>%
+                               dplyr::filter(estimate_name == "person_days") %>%
+                               dplyr::pull("estimate_value"))) ==
+                as.numeric(difftime(as.Date("2005-08-11"),
                                              as.Date("2005-06-19"))) - 30 +
                 as.numeric(difftime(as.Date("2015-01-02"),
                                     as.Date("2009-04-10"))) +
@@ -2617,8 +2771,13 @@ test_that("mock db: multiple observation periods", {
   )
 
   # we should have 2 events with washout 30
-  expect_true(sum(inc_PreWashEv$n_events) == 2)
-  expect_true(inc_PreWashEv %>% dplyr::select(person_days) %>% sum() ==
+  expect_true(sum(as.numeric(inc_PreWashEv %>%
+                               dplyr::filter(estimate_name == "outcome_count") %>%
+                               dplyr::pull("estimate_value"))) == 2)
+
+  expect_true(sum(as.numeric(inc_PreWashEv %>%
+                               dplyr::filter(estimate_name == "person_days") %>%
+                               dplyr::pull("estimate_value"))) ==
                 as.numeric(difftime(as.Date("2005-08-11"),
                                     as.Date("2005-07-19"))) - 30 + 7 +
                 as.numeric(difftime(as.Date("2015-01-02"),
@@ -2709,7 +2868,9 @@ test_that("mock db: multiple observation periods", {
   )
 
   # we should have 5 events with washout 1
-  expect_true(sum(inc_3op$n_events) == 5)
+  expect_true(sum(as.numeric(inc_3op %>%
+                               dplyr::filter(estimate_name == "outcome_count") %>%
+                               dplyr::pull("estimate_value"))) == 5)
 
   # try repeated events FALSE.
   inc_repev <- estimateIncidence(cdm,
@@ -2720,9 +2881,9 @@ test_that("mock db: multiple observation periods", {
                                  completeDatabaseIntervals = FALSE,
                                  minCellCount = 0
   )
-
-  # we should have 1 event,
-  expect_true(sum(inc_repev$n_events) == 2)
+  expect_true(sum(as.numeric(inc_repev %>%
+                               dplyr::filter(estimate_name == "outcome_count") %>%
+                               dplyr::pull("estimate_value"))) == 2)
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -2746,12 +2907,21 @@ test_that("mock db: check confidence intervals", {
                            completeDatabaseIntervals = TRUE
   )
 
-  expect_equal(inc$incidence_100000_pys_95CI_lower,
-               epitools::pois.exact(inc$n_events, inc$person_years)$lower * 100000,
+  pkg_est <- inc %>%
+    dplyr::select("estimate_name",
+                  "estimate_value", "additional_level") |>
+    tidyr::pivot_wider(names_from = "estimate_name",
+                       values_from = "estimate_value") |>
+    dplyr::filter(denominator_count > 1)
+
+  expect_equal(as.numeric(pkg_est$incidence_100000_pys_95CI_lower),
+               epitools::pois.exact(as.numeric(pkg_est$outcome_count),
+                                    as.numeric(pkg_est$person_years))$lower* 100000 ,
                tolerance = 1e-2
   )
-  expect_equal(inc$incidence_100000_pys_95CI_upper,
-               epitools::pois.exact(inc$n_events, inc$person_years)$upper * 100000,
+  expect_equal(as.numeric(pkg_est$incidence_100000_pys_95CI_upper),
+               epitools::pois.exact(as.numeric(pkg_est$outcome_count),
+                                    as.numeric(pkg_est$person_years))$upper* 100000 ,
                tolerance = 1e-2
   )
 
@@ -2773,18 +2943,20 @@ test_that("mock db: check attrition", {
   # for female cohort we should have a row for those excluded for not being male
   expect_true(any("Not Female" ==
                     attrition(inc) %>%
+                    dplyr::left_join(settings(inc), by = "result_id") |>
                     dplyr::filter(denominator_sex == "Female") %>%
                     dplyr::pull(.data$reason)))
   # for male, the opposite
   expect_true(any("Not Male" == attrition(inc) %>%
+                    dplyr::left_join(settings(inc), by = "result_id") |>
                     dplyr::filter(denominator_sex == "Male") %>%
                     dplyr::pull(.data$reason)))
 
   # check we can pick out specific analysis attrition
   expect_true(nrow(attrition(inc) %>%
-                     dplyr::filter(analysis_id == 1)) > 1)
+                     dplyr::filter(result_id == 1)) > 1)
   expect_true(nrow(attrition(inc) %>%
-                     dplyr::filter(analysis_id == 2)) > 1)
+                     dplyr::filter(result_id == 2)) > 1)
   CDMConnector::cdm_disconnect(cdm)
 
   # check obscuring counts
@@ -2875,25 +3047,7 @@ test_that("mock db: check compute permanent", {
     outcomeTable = "outcome",
     interval = "overall"
   )
-  # if using temp tables
-  # we have temp tables created by dbplyr
-  # expect_true(any(stringr::str_starts(
-  #   CDMConnector::listTables(attr(attr(cdm, "cdm_source"), "dbcon")),
-  #   "dbplyr_"
-  # )))
-  CDMConnector::cdm_disconnect(cdm)
-
-  # using permanent
-  cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
-  cdm <- generateDenominatorCohortSet(cdm = cdm, name = "denominator")
-  inc <- estimateIncidence(
-    cdm = cdm,
-    denominatorTable = "denominator",
-    outcomeTable = "outcome",
-    interval = "overall"
-  )
-
-  # no temp tables created by dbplyr
+  # we have no temp tables created by dbplyr
   expect_false(any(stringr::str_starts(
     CDMConnector::listTables(attr(attr(cdm, "cdm_source"), "dbcon")),
     "dbplyr_"
@@ -2927,14 +3081,23 @@ test_that("mock db: empty outcome cohort", {
     cdm = cdm,
     denominatorTable = "denominator",
     outcomeTable = "outcome",
-    interval = "months"
+    interval = "months",
+    minCellCount = 0
   ))
-  expect_true(sum(inc$n_events)==0)
+  expect_true(sum(as.numeric(inc |>
+    dplyr::filter(estimate_name == "outcome_count") |>
+    dplyr::pull("estimate_value")))==0)
 
   # make sure we also have a confidence interval even in the case of an empty outcome cohort
-  expect_true(all(inc$incidence_100000_pys == 0))
-  expect_true(all(inc$incidence_100000_pys_95CI_lower == 0))
-  expect_true(all(inc$incidence_100000_pys_95CI_upper > 0))
+  expect_true(all(inc |>
+                  dplyr::filter(estimate_name == "incidence_100000_pys") |>
+                  dplyr::pull("estimate_value")=="0"))
+  expect_true(all(as.numeric(inc |>
+                    dplyr::filter(estimate_name == "incidence_100000_pys_95CI_lower") |>
+                    dplyr::pull("estimate_value")) == 0))
+  expect_true(all(as.numeric(inc |>
+                               dplyr::filter(estimate_name == "incidence_100000_pys_95CI_upper") |>
+                               dplyr::pull("estimate_value")) > 0))
 
   CDMConnector::cdm_disconnect(cdm)
 
@@ -2961,7 +3124,8 @@ test_that("mock db: incidence using strata vars", {
   cdm$denominator <- cdm$denominator %>%
     dplyr::mutate(my_strata = dplyr::if_else(year(cohort_start_date) < 1995L,
                                              "first", "second")) %>%
-    dplyr::compute()
+    dplyr::compute(name = "denominator",
+                   temporary = FALSE)
 
   inc <- estimateIncidence(
     cdm = cdm,
@@ -2970,28 +3134,23 @@ test_that("mock db: incidence using strata vars", {
     interval = "months",
     strata = list(c("my_strata"))
   )
-  expect_true(all(c("strata_name", "strata_level") %in%
-                    colnames(inc)))
-  expect_true(all(c("Overall", "my_strata") %in%
-                    unique(inc %>%
-                             dplyr::pull("strata_name"))))
-  expect_true(all(c("Overall",
+  expect_true(all(c("overall", "my_strata") %in%
+                    unique(inc$strata_name)))
+  expect_true(all(c("overall",
                     "first", "second") %in%
-                    unique(inc %>%
-                             dplyr::pull("strata_level"))))
+                    unique(inc$strata_level)))
 
   # original without strata should be the same as "Overall" strata
   expect_equal(inc_orig,
                inc %>%
-                 dplyr::filter(strata_name == "Overall") %>%
-                 dplyr::select(!c("strata_name", "strata_level")))
+                 dplyr::filter(strata_level == "overall"))
 
 
   cdm$denominator <- cdm$denominator %>%
     dplyr::mutate(my_strata2 =  dplyr::if_else(month(cohort_start_date)<7,
                                                "a", "b")) %>%
-    dplyr::compute()
-
+    dplyr::compute(name = "denominator",
+                   temporary = FALSE)
   inc2 <- estimateIncidence(
     cdm = cdm,
     denominatorTable = "denominator",
@@ -2999,16 +3158,15 @@ test_that("mock db: incidence using strata vars", {
     interval = "months",
     strata = list(c("my_strata","my_strata2"))
   )
-  expect_true(all(c("strata_name", "strata_level") %in%
-                    colnames(inc2)))
-  expect_true(all(c("Overall", "my_strata and my_strata2") %in%
-                    unique(inc2 %>%
-                             dplyr::pull("strata_name"))))
-  expect_true(all(c("Overall",
-                    "first and a", "first and b",
-                    "second and a", "second and b") %in%
-                    unique(inc2 %>%
-                             dplyr::pull("strata_level"))))
+
+  expect_true(all(c("overall", "my_strata &&& my_strata2") %in%
+                    unique(inc2$strata_name)))
+  expect_true(all(c("overall",
+                    "first &&& a",
+                    "second &&& a",
+                    "first &&& b",
+                    "second &&& b") %in%
+                    unique(inc2$strata_level)))
 
   inc3 <- estimateIncidence(
     cdm = cdm,
@@ -3019,20 +3177,19 @@ test_that("mock db: incidence using strata vars", {
                   c("my_strata2"),
                   c("my_strata", "my_strata2")))
 
-  expect_true(all(c("strata_name", "strata_level") %in%
-                    colnames(inc3)))
-  expect_true(all(c("Overall", "my_strata", "my_strata2",
-                    "my_strata and my_strata2") %in%
-                    unique(inc3 %>%
-                             dplyr::pull("strata_name"))))
-  expect_true(all(c("Overall",
+  expect_true(all(c("overall",
+                    "my_strata"  ,
+                    "my_strata2"  ,
+                    "my_strata &&& my_strata2") %in%
+                    unique(inc3$strata_name)))
+  expect_true(all(c("overall",
                     "first", "second",
                     "a", "b",
-                    "first and a", "first and b",
-                    "second and a", "second and b") %in%
-                    unique(inc3 %>%
-                             dplyr::pull("strata_level"))))
-
+                    "first &&& a",
+                    "second &&& a",
+                    "first &&& b",
+                    "second &&& b") %in%
+                    unique(inc3$strata_level)))
 
   # without overall strata
   inc4 <- estimateIncidence(
@@ -3044,20 +3201,18 @@ test_that("mock db: incidence using strata vars", {
                   c("my_strata2"),
                   c("my_strata", "my_strata2")),
     includeOverallStrata = FALSE)
-  expect_false("Overall" %in% unique(inc4 %>%
-                                       dplyr::pull("strata_name")))
-  expect_true(all(c("my_strata", "my_strata2",
-                    "my_strata and my_strata2") %in%
-                    unique(inc4 %>%
-                             dplyr::pull("strata_name"))))
-  expect_false("Overall" %in% unique(inc4 %>%
-                                       dplyr::pull("strata_level")))
+  expect_true(all(c("my_strata"  ,
+                    "my_strata2"  ,
+                    "my_strata &&& my_strata2") %in%
+                    unique(inc4$strata_name)))
   expect_true(all(c("first", "second",
                     "a", "b",
-                    "first and a", "first and b",
-                    "second and a", "second and b") %in%
-                    unique(inc4 %>%
-                             dplyr::pull("strata_level"))))
+                    "first &&& a",
+                    "second &&& a",
+                    "first &&& b",
+                    "second &&& b") %in%
+                    unique(inc4$strata_level)))
+
 
   expect_error(estimateIncidence(
     cdm = cdm,
@@ -3153,98 +3308,22 @@ test_that("mock db: multiple outcome cohort id", {
 
   expect_equal(
     inc_all_outcome %>%
-      dplyr::filter(outcome_cohort_id == 1) %>%
-      dplyr::pull("incidence_100000_pys"),
-    inc_all_outcome_1 %>%
-      dplyr::pull("incidence_100000_pys")
+      dplyr::filter(variable_level == "cohort_1")|>
+      dplyr::pull("estimate_value"),
+    inc_all_outcome_1 |>
+      dplyr::pull("estimate_value")
   )
 
+
   expect_equal(
     inc_all_outcome %>%
-      dplyr::filter(outcome_cohort_id == 2) %>%
-      dplyr::pull("incidence_100000_pys"),
-    inc_all_outcome_2 %>%
-      dplyr::pull("incidence_100000_pys")
+      dplyr::filter(variable_level == "cohort_2")|>
+      dplyr::pull("estimate_value"),
+    inc_all_outcome_2 |>
+      dplyr::pull("estimate_value")
   )
 
   CDMConnector::cdm_disconnect(cdm)
 
 })
 
-test_that("test summarised result working", {
-#
-#   cdm <- mockIncidencePrevalenceRef()
-#
-#   cdm <- generateDenominatorCohortSet(cdm = cdm, name = "denominator")
-#
-#   inc <- estimateIncidence(
-#     cdm = cdm,
-#     denominatorTable = "denominator",
-#     outcomeTable = "outcome",
-#     interval = "months",
-#     summarisedResult = TRUE
-#   )
-#
-#   expect_true("summarised_result" %in% class(inc))
-#   expect_no_error(visOmopResults::tidy(inc))
-#   expect_true(
-#     all(colnames(settings(inc)) %in% c(
-#       'result_id', 'result_type', 'package_name', 'package_version', 'analysis_interval',
-#       'analysis_complete_database_intervals', 'denominator_cohort_name', 'denominator_age_group',
-#       'denominator_sex', 'denominator_days_prior_observation', 'denominator_start_date',
-#       'denominator_end_date', 'denominator_target_cohort_name', 'outcome_cohort_name', "min_cell_count"
-#     ))
-#   )
-#   expect_true(unique(settings(inc)$result_type) == "incidence")
-#
-#   # strata
-#   cdm$denominator <- cdm$denominator %>%
-#     dplyr::mutate(my_strata = dplyr::if_else(year(cohort_start_date) < 1995L,
-#                                              "first", "second")) %>%
-#     dplyr::compute()
-#   inc <- estimateIncidence(
-#     cdm = cdm,
-#     denominatorTable = "denominator",
-#     outcomeTable = "outcome",
-#     interval = "months",
-#     strata = list(c("my_strata")),
-#     summarisedResult = TRUE,
-#     minCellCount = 0
-#   )
-#   expect_true("summarised_result" %in% class(inc))
-#   inc2 <- estimateIncidence(
-#     cdm = cdm,
-#     denominatorTable = "denominator",
-#     outcomeTable = "outcome",
-#     interval = "months",
-#     strata = list(c("my_strata")),
-#     minCellCount = 0
-#   )
-#   expect_equal(
-#     attrition(inc2) |>
-#       dplyr::mutate(result_id = as.integer(analysis_id)) |>
-#       dplyr::select(result_id, number_records, number_subjects, reason_id, reason, excluded_records, excluded_subjects),
-#     attrition(inc)
-#   )
-#
-#   inc_tidy <- inc |> visOmopResults::tidy()
-#   inc_sup <- inc |> omopgenerics::suppress() |> visOmopResults::tidy()
-#   expect_no_error(inc_tidy <- visOmopResults::tidy(inc, addSettings = FALSE))
-#   expect_true(nrow(inc_tidy) == nrow(inc2))
-#   expect_true(all(c("my_strata") %in% colnames(inc_tidy)))
-#   expect_true(all(c("overall", "first") %in%
-#                     unique(inc_tidy %>%
-#                              dplyr::pull("my_strata"))))
-#   # suppress
-#   sup <- inc_tidy |> dplyr::filter(denominator_count < 5 & denominator_count > 0) |>
-#     dplyr::select(!c("denominator_count", "outcome_count", "incidence_100000_pys", "incidence_100000_pys_95CI_lower",
-#                      "incidence_100000_pys_95CI_upper", "person_days", "person_years"))
-#
-#   expect_true(all(is.na(inc_sup |> dplyr::inner_join(sup) |> dplyr::pull("denominator_count"))))
-#   expect_true(all(is.na(inc_sup |> dplyr::inner_join(sup) |> dplyr::pull("person_days"))))
-#   expect_true(all(is.na(inc_sup |> dplyr::inner_join(sup) |> dplyr::pull("incidence_100000_pys"))))
-#   expect_false(any(is.na(inc_sup |> dplyr::inner_join(sup) |> dplyr::pull("outcome_count"))))
-#
-#   CDMConnector::cdm_disconnect(cdm)
-#
-})
diff --git a/tests/testthat/test-estimatePrevalence.R b/tests/testthat/test-estimatePrevalence.R
index 03f02df..020810c 100644
--- a/tests/testthat/test-estimatePrevalence.R
+++ b/tests/testthat/test-estimatePrevalence.R
@@ -8,69 +8,10 @@ test_that("mock db: check output format", {
     outcomeTable = "outcome",
     interval = "years"
   )
-
-  # check estimates tibble
-  expect_true(all(c(
-    "analysis_id",
-    "n_cases",
-    "n_population",
-    "prevalence",
-    "prevalence_95CI_lower",
-    "prevalence_95CI_upper",
-    "prevalence_start_date",
-    "prevalence_end_date",
-    "population_obscured",
-    "cases_obscured",
-    "result_obscured",
-    "outcome_cohort_id",
-    "outcome_cohort_name",
-    "analysis_type",
-    "analysis_time_point",
-    "analysis_interval",
-    "analysis_full_contribution",
-    "analysis_complete_database_intervals",
-    "analysis_min_cell_count",
-    "denominator_cohort_id",
-    "denominator_age_group",
-    "denominator_sex",
-    "denominator_days_prior_observation",
-    "denominator_start_date",
-    "denominator_end_date",
-    "denominator_target_cohort_definition_id",
-    "denominator_target_cohort_name",
-    "cdm_name"
-  ) %in%
-    names(prev)))
-
-  expect_true(all(c(
-    "analysis_id", "number_records", "number_subjects",
-    "reason_id", "reason",
-    "excluded_records", "excluded_subjects",
-    "outcome_cohort_id",
-    "outcome_cohort_name",
-    "analysis_type",
-    "analysis_time_point",
-    "analysis_interval",
-    "analysis_full_contribution",
-    "analysis_complete_database_intervals",
-    "analysis_min_cell_count",
-    "denominator_cohort_id",
-    "denominator_age_group",
-    "denominator_sex",
-    "denominator_days_prior_observation",
-    "denominator_start_date",
-    "denominator_end_date",
-    "denominator_target_cohort_definition_id",
-    "denominator_target_cohort_name",
-    "cdm_name"
-  ) %in%
-    names(attrition(prev))))
-
   my_settings <- settings(prev)
   expect_true(nrow(my_settings) > 0)
 
   CDMConnector::cdm_disconnect(cdm)
-
 })
 
 test_that("mock db: checks on working example", {
@@ -246,41 +187,55 @@ test_that("mock db: check minimum counts", {
     type = "period",
     interval = "months"
   )
-  expect_true(prev$n_cases[1] == 17)
-  expect_true(prev$n_cases[2] == 3)
-  expect_true(prev$n_cases[3] == 0)
-  expect_true(prev$n_population[1] == 20)
-  expect_true(prev$n_population[2] == 3)
-  expect_true(prev$n_population[3] == 3)
-  expect_true(!is.na(prev$prevalence[1]))
-  expect_true(!is.na(prev$prevalence[2]))
-  expect_true(!is.na(prev$prevalence[3]))
-  expect_true(!is.na(prev$prevalence_95CI_lower[1]))
-  expect_true(!is.na(prev$prevalence_95CI_upper[1]))
-
-  prev <- estimatePrevalence(
-    cdm = cdm,
-    denominatorTable = "denominator",
-    outcomeTable = "outcome",
-    minCellCount = 5,
-    type = "period",
-    interval = "months"
-  )
-  expect_true(prev$n_cases[1] == 17)
-  expect_true(is.na(prev$n_cases[2]))
-  expect_true(!is.na(prev$n_cases[3])) # don't suppress zero
-  expect_true(prev$n_population[1] == 20)
-  expect_true(is.na(prev$n_population[2]))
-  expect_true(is.na(prev$n_population[3]))
-  expect_true(!is.na(prev$prevalence[1]))
-  expect_true(is.na(prev$prevalence[2]))
-  expect_true(is.na(prev$prevalence[3]))
-  expect_true(!is.na(prev$prevalence_95CI_lower[1]))
-  expect_true(is.na(prev$prevalence_95CI_lower[2]))
-  expect_true(is.na(prev$prevalence_95CI_lower[3]))
-  expect_true(!is.na(prev$prevalence_95CI_upper[1]))
-  expect_true(is.na(prev$prevalence_95CI_upper[2]))
-  expect_true(is.na(prev$prevalence_95CI_upper[3]))
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "outcome_count")
+  expect_true(prev_est$estimate_value[1] == "17")
+  expect_true(prev_est$estimate_value[2] == "3")
+  expect_true(prev_est$estimate_value[3] == "0")
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "denominator_count")
+  expect_true(prev_est$estimate_value[1] == "20")
+  expect_true(prev_est$estimate_value[2] == "3")
+  expect_true(prev_est$estimate_value[3] == "3")
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "prevalence")
+  expect_true(!is.na(prev_est$estimate_value[1]))
+  expect_true(!is.na(prev_est$estimate_value[2]))
+  expect_true(!is.na(prev_est$estimate_value[3]))
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "prevalence_95CI_lower")
+  expect_true(!is.na(prev_est$estimate_value[1]))
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "prevalence_95CI_upper")
+  expect_true(!is.na(prev_est$estimate_value[1]))
+
+  # suppress results
+  prev <- omopgenerics::suppress(prev, minCellCount = 5)
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "outcome_count")
+  expect_true(prev_est$estimate_value[1] == "17")
+  expect_true(is.na(prev_est$estimate_value[2]))
+  expect_true(prev_est$estimate_value[3] == "0") # don't suppress zero
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "denominator_count")
+  expect_true(prev_est$estimate_value[1] == "20")
+  expect_true(is.na(prev_est$estimate_value[2]))
+  expect_true(is.na(prev_est$estimate_value[3]))
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "prevalence")
+  expect_true(!is.na(prev_est$estimate_value[1]))
+  expect_true(is.na(prev_est$estimate_value[2]))
+  expect_true(is.na(prev_est$estimate_value[3]))
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "prevalence_95CI_lower")
+  expect_true(!is.na(prev_est$estimate_value[1]))
+  expect_true(is.na(prev_est$estimate_value[2]))
+  expect_true(is.na(prev_est$estimate_value[3]))
+  prev_est <- prev |>
+    dplyr::filter(estimate_name == "prevalence_95CI_upper")
+  expect_true(!is.na(prev_est$estimate_value[1]))
+  expect_true(is.na(prev_est$estimate_value[2]))
+  expect_true(is.na(prev_est$estimate_value[3]))
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -333,7 +288,9 @@ test_that("mock db: check study time periods", {
   # we expect 12 months of which the last in December
   # the last month should also be included
   # as the person goes up to the last day of the month
-  expect_true(nrow(prev) == 12)
+  expect_true(nrow(prev |>
+                     dplyr::filter(
+                       estimate_name == "outcome_count")) == 12)
 
 
   # overall period
@@ -344,7 +301,8 @@ test_that("mock db: check study time periods", {
                              interval = "overall"
   )
   # just one row
-  expect_true(nrow(prev) == 1)
+  expect_true(nrow(prev |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 1)
 
   CDMConnector::cdm_disconnect(cdm)
 
@@ -392,7 +350,8 @@ test_that("mock db: check study time periods", {
                              fullContribution = TRUE,
                              interval = "weeks"
   )
-  expect_true(nrow(prev) == 45)
+  expect_true(nrow(prev |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 45)
 
   prev <- estimatePrevalence(cdm,
                              denominatorTable = "denominator",
@@ -401,7 +360,8 @@ test_that("mock db: check study time periods", {
                              fullContribution = TRUE,
                              interval = "months"
   )
-  expect_true(nrow(prev) == 10)
+  expect_true(nrow(prev |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 10)
 
   prev <- estimatePrevalence(cdm,
                              denominatorTable = "denominator",
@@ -410,7 +370,8 @@ test_that("mock db: check study time periods", {
                              fullContribution = TRUE,
                              interval = "years"
   )
-  expect_true(nrow(prev) == 0)
+  expect_true(nrow(prev |>
+                     dplyr::filter(estimate_name == "outcome_count")) == 0)
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -470,7 +431,10 @@ test_that("mock db: check fullContribution requirement", {
                              completeDatabaseIntervals = FALSE,
                              minCellCount = 0
   )
-  expect_true(all(prev$n_population == 2))
+  expect_true(all(prev |>
+    dplyr::filter(
+      estimate_name == "denominator_count") |>
+     dplyr::pull("estimate_value") == "2"))
 
   prev <- estimatePrevalence(cdm,
                              denominatorTable = "denominator",
@@ -481,8 +445,10 @@ test_that("mock db: check fullContribution requirement", {
                              completeDatabaseIntervals = FALSE,
                              minCellCount = 0
   )
-
-  expect_true(all(prev$n_population == c(2, 1, 1)))
+  expect_true(all(prev |>
+                    dplyr::filter(
+                      estimate_name == "denominator_count") |>
+                    dplyr::pull("estimate_value") == c("2", "1", "1")))
 
   expect_true(attrition(prev) %>%
                 dplyr::filter(reason ==
@@ -545,9 +511,15 @@ test_that("mock db: check periods follow calendar dates", {
                               fullContribution = FALSE,
                               completeDatabaseIntervals = FALSE
   )
-  expect_true(nrow(prev1) == 4)
-  expect_true(all(lubridate::year(prev1$prevalence_start_date) ==
-                    c("2010", "2011", "2012", "2013")))
+  expect_true(nrow(prev1 |>
+                     dplyr::filter(
+                       estimate_name == "denominator_count")) == 4)
+  expect_true(all(lubridate::year(
+    prev1 |> visOmopResults::splitAdditional() |>
+    dplyr::filter(
+      estimate_name == "denominator_count") |>
+    dplyr::pull("prevalence_start_date")) ==
+      c("2010", "2011", "2012", "2013")))
 
   prev2 <- estimatePrevalence(cdm,
                               denominatorTable = "denominator",
@@ -558,9 +530,12 @@ test_that("mock db: check periods follow calendar dates", {
                               fullContribution = FALSE,
                               completeDatabaseIntervals = TRUE
   )
-  expect_true(nrow(prev2) == 2)
-  expect_true(all(lubridate::year(prev2$prevalence_start_date) ==
-                    c("2011", "2012")))
+  expect_true(all(lubridate::year(
+    prev2 |> visOmopResults::splitAdditional() |>
+      dplyr::filter(
+        estimate_name == "denominator_count") |>
+      dplyr::pull("prevalence_start_date")) ==
+      c("2011", "2012")))
 
   # for months
   cdm <- generateDenominatorCohortSet(
@@ -578,8 +553,13 @@ test_that("mock db: check periods follow calendar dates", {
                              fullContribution = FALSE,
                              completeDatabaseIntervals = FALSE
   )
-  expect_true(prev$prevalence_start_date[1] ==
-                as.Date("2011-01-15"))
+
+  expect_true(prev |> visOmopResults::splitAdditional() |>
+    dplyr::filter(
+      estimate_name == "denominator_count") |>
+    head(1) |>
+    dplyr::pull("prevalence_start_date") ==
+     "2011-01-15")
   # where we expect the study to start the next month
   prev <- estimatePrevalence(cdm,
                              denominatorTable = "denominator",
@@ -590,8 +570,12 @@ test_that("mock db: check periods follow calendar dates", {
                              fullContribution = FALSE,
                              completeDatabaseIntervals = TRUE
   )
-  expect_true(prev$prevalence_start_date[1] ==
-                as.Date("2011-02-01"))
+  expect_true(prev |> visOmopResults::splitAdditional() |>
+                dplyr::filter(
+                  estimate_name == "denominator_count") |>
+                head(1) |>
+                dplyr::pull("prevalence_start_date") ==
+                "2011-02-01")
 
   # for overall
   prev <- estimatePrevalence(cdm,
@@ -603,10 +587,18 @@ test_that("mock db: check periods follow calendar dates", {
                              fullContribution = FALSE,
                              completeDatabaseIntervals = FALSE
   )
-  expect_true(prev$prevalence_start_date[1] ==
-                as.Date("2011-01-15"))
-  expect_true(prev$prevalence_end_date[1] ==
-                as.Date("2013-06-15"))
+  expect_true(prev |> visOmopResults::splitAdditional() |>
+                dplyr::filter(
+                  estimate_name == "denominator_count") |>
+                head(1) |>
+                dplyr::pull("prevalence_start_date") ==
+                "2011-01-15")
+  expect_true(prev |> visOmopResults::splitAdditional() |>
+                dplyr::filter(
+                  estimate_name == "denominator_count") |>
+                head(1) |>
+                dplyr::pull("prevalence_end_date") ==
+                "2013-06-15")
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -653,7 +645,11 @@ test_that("mock db: check multiple outcome ids", {
     interval = "years",
     minCellCount = 0
   )
-  expect_true(all(prev[["n_cases"]] == 1))
+
+  expect_true(all(prev |>
+    dplyr::filter(
+      estimate_name == "outcome_count") |>
+    dplyr::pull("estimate_value") == "1"))
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -758,7 +754,6 @@ test_that("mock db: check expected errors", {
     denominatorCohortId = 1
   ))
 
-
   CDMConnector::cdm_disconnect(cdm)
 })
 
@@ -902,19 +897,28 @@ test_that("mock db: multiple observation periods", {
   )
   # nobody should appear in 2006
   expect_true(ppe %>%
-                dplyr::filter(prevalence_start_date == "2006-01-01") %>%
-                dplyr::pull("n_population") == 0)
+                visOmopResults::splitAdditional() |>
+                dplyr::filter(prevalence_start_date == "2006-01-01",
+                              estimate_name == "denominator_count") %>%
+                dplyr::pull("estimate_value") == 0)
   expect_true(ppe %>%
-                dplyr::filter(prevalence_start_date == "2006-01-01") %>%
-                dplyr::pull("n_cases") == 0)
+                visOmopResults::splitAdditional() |>
+                dplyr::filter(prevalence_start_date == "2006-01-01",
+                              estimate_name == "outcome_count") %>%
+                dplyr::pull("estimate_value") == 0)
 
   # one person with an event in 2005
   expect_true(ppe %>%
-                dplyr::filter(lubridate::year(prevalence_start_date) == "2005") %>%
-                dplyr::pull("n_population") == 1)
+                visOmopResults::splitAdditional() |>
+                dplyr::filter(lubridate::year(prevalence_start_date) == "2005",
+                              estimate_name == "denominator_count") %>%
+                dplyr::pull("estimate_value") == "1")
   expect_true(ppe %>%
-                dplyr::filter(lubridate::year(prevalence_start_date) == "2005") %>%
-                dplyr::pull("n_cases") == 1)
+                visOmopResults::splitAdditional() |>
+                dplyr::filter(lubridate::year(prevalence_start_date) == "2005",
+                              estimate_name == "outcome_count") %>%
+                dplyr::pull("estimate_value") == "1")
+
 
   # as for point prevalence, we would expect no positive n_cases at default
   ppo <- estimatePointPrevalence(
@@ -924,7 +928,11 @@ test_that("mock db: multiple observation periods", {
     interval = "months",
     minCellCount = 0
   )
-  expect_true(sum(ppo$n_cases) == 0)
+
+  expect_true(sum(as.numeric(ppo %>%
+    visOmopResults::splitAdditional() |>
+    dplyr::filter(estimate_name == "outcome_count") %>%
+    dplyr::pull("estimate_value"))) == 0)
 
   CDMConnector::cdm_disconnect(cdm)
 })
@@ -941,20 +949,29 @@ test_that("mock db: check confidence intervals", {
                              type = "point",
                              interval = "years",
                              minCellCount = 0
-  ) %>%
-    dplyr::filter(n_population > 1)
+  )
+
+ pkg_est <- prev %>%
+    dplyr::select("estimate_name",
+                  "estimate_value", "additional_level") |>
+    tidyr::pivot_wider(names_from = "estimate_name",
+                       values_from = "estimate_value") |>
+      dplyr::filter(denominator_count > 1)
 
   # compare our wilson CIs with those from Hmisc
-  hmisc_ci <- Hmisc::binconf(prev$n_cases, prev$n_population,
+  hmisc_ci <- Hmisc::binconf(as.numeric(pkg_est$outcome_count),
+                             as.numeric(pkg_est$denominator_count),
                              alpha = 0.05,
                              method = c("wilson"),
                              return.df = TRUE
   )
 
-  expect_equal(prev$prevalence_95CI_lower, hmisc_ci$Lower,
+  expect_equal(as.numeric(pkg_est$prevalence_95CI_lower),
+               hmisc_ci$Lower,
                tolerance = 1e-2
   )
-  expect_equal(prev$prevalence_95CI_upper, hmisc_ci$Upper,
+  expect_equal(as.numeric(pkg_est$prevalence_95CI_upper),
+               hmisc_ci$Upper,
                tolerance = 1e-2
   )
 
@@ -978,19 +995,17 @@ test_that("mock db: check attrition", {
 
   # for female cohort we should have a row for those excluded for not being male
   expect_true(any("Not Female" == attrition(prev) %>%
+                    dplyr::left_join(settings(prev),
+                                     by = "result_id") |>
                     dplyr::filter(denominator_sex == "Female") %>%
                     dplyr::pull(.data$reason)))
   # for male, the opposite
   expect_true(any("Not Male" == attrition(prev) %>%
+                    dplyr::left_join(settings(prev),
+                                     by = "result_id") |>
                     dplyr::filter(denominator_sex == "Male") %>%
                     dplyr::pull(.data$reason)))
 
-  # check we can pick out specific analysis attrition
-  expect_true(nrow(attrition(prev) %>%
-                     dplyr::filter(analysis_id == 1)) > 1)
-  expect_true(nrow(attrition(prev) %>%
-                     dplyr::filter(analysis_id == 2)) > 1)
-
   CDMConnector::cdm_disconnect(cdm)
 })
 
@@ -1076,7 +1091,6 @@ test_that("mock db: check compute permanent", {
 
   # using permanent (no prefix)
   cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
-  attr(attr(cdm, "cdm_source"), "write_schema") <- "main"
 
   cdm <- generateDenominatorCohortSet(
     cdm = cdm, name = "dpop"
@@ -1129,7 +1143,8 @@ test_that("mock db: test empty outcome table works", {
 
   cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
 
-  cdm[["outcome"]] <- cdm[["outcome"]] %>% dplyr::filter(cohort_definition_id == 33)
+  cdm[["outcome"]] <- cdm[["outcome"]] %>%
+    dplyr::filter(cohort_definition_id == 33)
 
   cdm <- generateDenominatorCohortSet(cdm = cdm, name = "denominator")
 
@@ -1140,6 +1155,7 @@ test_that("mock db: test empty outcome table works", {
     interval = "years"
   ))
 
+  CDMConnector::cdm_disconnect(cdm)
 })
 
 test_that("mock db: prevalence using strata vars", {
@@ -1160,7 +1176,8 @@ test_that("mock db: prevalence using strata vars", {
   cdm$denominator <- cdm$denominator %>%
     dplyr::mutate(my_strata = dplyr::if_else(year(cohort_start_date) < 1990,
                                              "first", "second")) %>%
-    dplyr::compute()
+    dplyr::compute(temporary = FALSE,
+                   name = "denominator")
 
   prev <- estimatePrevalence(
     cdm = cdm,
@@ -1169,27 +1186,21 @@ test_that("mock db: prevalence using strata vars", {
     interval = "years",
     strata = list(c("my_strata"))
   )
-  expect_true(all(c("strata_name", "strata_level") %in%
-                    colnames(prev)))
-  expect_true(all(c("Overall", "my_strata") %in%
-                    unique(prev %>%
-                             dplyr::pull("strata_name"))))
-  expect_true(all(c("Overall",
-                    "first", "second") %in%
-                    unique(prev %>%
-                             dplyr::pull("strata_level"))))
+
+  expect_true(all(c("overall", "first", "second") %in%
+  unique(prev |> dplyr::pull("strata_level"))))
 
   # original without strata should be the same as "Overall" strata
   expect_equal(prev_orig,
                prev %>%
-                 dplyr::filter(strata_name == "Overall") %>%
-                 dplyr::select(!c("strata_name", "strata_level")))
+                 dplyr::filter(strata_level == "overall"))
 
 
   cdm$denominator <- cdm$denominator %>%
     dplyr::mutate(my_strata2 =  dplyr::if_else(month(cohort_start_date)<7,
                                                "a", "b")) %>%
-    dplyr::compute()
+    dplyr::compute(temporary = FALSE,
+                   name = "denominator")
 
   prev2 <- estimatePrevalence(
     cdm = cdm,
@@ -1198,16 +1209,12 @@ test_that("mock db: prevalence using strata vars", {
     interval = "years",
     strata = list(c("my_strata","my_strata2"))
   )
-  expect_true(all(c("strata_name", "strata_level") %in%
-                    colnames(prev2)))
-  expect_true(all(c("Overall", "my_strata and my_strata2") %in%
-                    unique(prev2 %>%
-                             dplyr::pull("strata_name"))))
-  expect_true(all(c("Overall",
-                    "first and a", "first and b",
-                    "second and a", "second and b") %in%
-                    unique(prev2 %>%
-                             dplyr::pull("strata_level"))))
+
+  expect_true(all(c("overall", "first &&& a",
+                    "first &&& b",
+                    "second &&& a",
+                    "second &&& b") %in%
+                    unique(prev2 |> dplyr::pull("strata_level"))))
 
   prev3 <- estimatePrevalence(
     cdm = cdm,
@@ -1218,19 +1225,15 @@ test_that("mock db: prevalence using strata vars", {
                   c("my_strata2"),
                   c("my_strata", "my_strata2")))
 
-  expect_true(all(c("strata_name", "strata_level") %in%
-                    colnames(prev3)))
-  expect_true(all(c("Overall", "my_strata", "my_strata2",
-                    "my_strata and my_strata2") %in%
-                    unique(prev3 %>%
-                             dplyr::pull("strata_name"))))
-  expect_true(all(c("Overall",
-                    "first", "second",
-                    "a", "b",
-                    "first and a", "first and b",
-                    "second and a", "second and b") %in%
-                    unique(prev3 %>%
-                             dplyr::pull("strata_level"))))
+  expect_true(all(c("overall",
+                    "first",
+                    "second",
+                    "first &&& a",
+                    "first &&& b",
+                    "second &&& a",
+                    "second &&& b") %in%
+                    unique(prev3 |> dplyr::pull("strata_level"))))
+
 
 
   # without overall strata
@@ -1243,20 +1246,15 @@ test_that("mock db: prevalence using strata vars", {
                   c("my_strata2"),
                   c("my_strata", "my_strata2")),
     includeOverallStrata = FALSE)
-  expect_false("Overall" %in% unique(prev4 %>%
-                                       dplyr::pull("strata_name")))
-  expect_true(all(c("my_strata", "my_strata2",
-                    "my_strata and my_strata2") %in%
-                    unique(prev4 %>%
-                             dplyr::pull("strata_name"))))
-  expect_false("Overall" %in% unique(prev4 %>%
-                                       dplyr::pull("strata_level")))
-  expect_true(all(c("first", "second",
-                    "a", "b",
-                    "first and a", "first and b",
-                    "second and a", "second and b") %in%
-                    unique(prev4 %>%
-                             dplyr::pull("strata_level"))))
+  expect_true(all(c("first",
+                    "second",
+                    "first &&& a",
+                    "first &&& b",
+                    "second &&& a",
+                    "second &&& b") %in%
+                    unique(prev4 |> dplyr::pull("strata_level"))))
+  expect_false(c("overall") %in%
+                    unique(prev4 |> dplyr::pull("strata_level")))
 
   expect_error(estimatePrevalence(
     cdm = cdm,
@@ -1282,105 +1280,4 @@ test_that("mock db: prevalence using strata vars", {
   CDMConnector::cdm_disconnect(cdm)
 })
 
-test_that("summarise result works", {
-
-  # cdm <- mockIncidencePrevalenceRef(sampleSize = 1000,
-  #                                   outPre = 0.7)
-  #
-  # cdm <- generateDenominatorCohortSet(cdm = cdm,
-  #                                     name = "denominator")
-  #
-  # prev_orig <- estimatePrevalence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   interval = "years"
-  # )
-  #
-  # cdm$denominator <- cdm$denominator %>%
-  #   dplyr::mutate(my_strata = dplyr::if_else(year(cohort_start_date) < 1990,
-  #                                            "first", "second")) %>%
-  #   dplyr::compute()
-  #
-  # prev_sr <- estimatePrevalence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   interval = "years",
-  #   strata = list(c("my_strata")),
-  #   summarisedResult = TRUE,
-  #   minCellCount = 0
-  # )
-  #
-  # expect_true("summarised_result" %in% class(prev_sr))
-  # # suppress
-  # prev_sup <- prev_sr |> omopgenerics::suppress(minCellCount = 2) |> visOmopResults::tidy()
-  # expect_no_error(prev_tidy <- visOmopResults::tidy(prev_sr, addSettings = FALSE))
-  # expect_true(all(c("my_strata") %in% colnames(prev_tidy)))
-  # expect_true(all(c("overall", "first") %in%
-  #                   unique(prev_tidy %>%
-  #                            dplyr::pull("my_strata"))))
-  # expect_true(
-  #   all(colnames(settings(prev_sr)) == c(
-  #     'result_id', 'result_type', 'package_name', 'package_version', 'analysis_type',
-  #     'analysis_interval', 'analysis_complete_database_intervals', 'analysis_full_contribution',
-  #     'denominator_cohort_name', 'denominator_age_group', 'denominator_sex',
-  #     'denominator_days_prior_observation', 'denominator_start_date', 'denominator_end_date',
-  #     'denominator_target_cohort_name', 'outcome_cohort_name', 'min_cell_count'
-  #   ))
-  # )
-  # expect_true(unique(settings(prev_sr)$result_type) == "point_prevalence")
-  #
-  # sup <- prev_tidy |> dplyr::filter(outcome_count < 2 & outcome_count > 0) |>
-  #   dplyr::select(!c("denominator_count", "outcome_count", "prevalence", "prevalence_95CI_lower", "prevalence_95CI_upper"))
-  #
-  # expect_true(all(is.na(prev_sup |> dplyr::inner_join(sup) |> dplyr::pull("outcome_count"))))
-  # expect_true(all(is.na(prev_sup |> dplyr::inner_join(sup) |> dplyr::pull("prevalence"))))
-  # expect_true(all(is.na(prev_sup |> dplyr::inner_join(sup) |> dplyr::pull("prevalence_95CI_upper"))))
-  # expect_false(any(is.na(prev_sup |> dplyr::inner_join(sup) |> dplyr::pull("denominator_count"))))
-  #
-  # prev <- estimatePrevalence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   interval = "years",
-  #   strata = list(c("my_strata")),
-  #   minCellCount = 0
-  # )
-  # expect_equal(
-  #   attrition(prev_sr),
-  #   attrition(prev) |>
-  #     dplyr::mutate(result_id = as.integer(analysis_id)) |>
-  #     dplyr::select(result_id, number_records, number_subjects, reason_id, reason, excluded_records, excluded_subjects))
-  # expect_true(nrow(prev_tidy) == nrow(prev))
-  #
-  # prev_sr <- estimatePeriodPrevalence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   interval = "years",
-  #   summarisedResult = TRUE
-  # )
-  # expect_true("summarised_result" %in% class(prev_sr))
-  # expect_no_error(prev_tidy <- visOmopResults::tidy(prev_sr, addSettings = FALSE))
-  # expect_true(
-  #   all(colnames(prev_tidy) == c(
-  #     'result_id', 'cdm_name', 'denominator_cohort_name', 'variable_name', 'variable_level',
-  #     'prevalence_start_date', 'prevalence_end_date', 'denominator_count', 'outcome_count', 'prevalence',
-  #     'prevalence_95CI_lower', 'prevalence_95CI_upper'
-  #   ))
-  # )
-  # expect_true(
-  #   all(colnames(settings(prev_sr)) == c(
-  #     'result_id', 'result_type', 'package_name', 'package_version', 'analysis_type',
-  #     'analysis_interval', 'analysis_complete_database_intervals', 'analysis_full_contribution',
-  #     'denominator_cohort_name', 'denominator_age_group', 'denominator_sex',
-  #     'denominator_days_prior_observation', 'denominator_start_date', 'denominator_end_date',
-  #     'denominator_target_cohort_name', 'outcome_cohort_name', 'min_cell_count'
-  #   ))
-  # )
-  # expect_true(unique(settings(prev_sr)$result_type) == "period_prevalence")
-  #
-  # CDMConnector::cdm_disconnect(cdm)
-})
 
diff --git a/tests/testthat/test-plotting.R b/tests/testthat/test-plotting.R
index 79ea81a..8ed9f68 100644
--- a/tests/testthat/test-plotting.R
+++ b/tests/testthat/test-plotting.R
@@ -1,239 +1,9 @@
-test_that("basic incidence plot", {
-  # skip_if_not_installed("ggplot2")
-  # skip_if_not_installed("scales")
-  #
-  # cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm, name = "denominator",
-  #   cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
-  # )
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE
-  # )
-  # plot <- plotIncidence(inc)
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # # with a different x axis
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm,name = "denominator",
-  #   ageGroup = list(
-  #     c(0, 30),
-  #     c(31, 100)
-  #   )
-  # )
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome", interval = "overall",
-  #   summarisedResult = TRUE
-  # )
-  # plot <- plotIncidence(inc, x = "denominator_age_group")
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # CDMConnector::cdm_disconnect(cdm)
-})
-
-test_that("basic prevalence plot", {
-  # skip_if_not_installed("ggplot2")
-  # skip_if_not_installed("scales")
-  # cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm, name = "denominator",
-  #   cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
-  # )
-  # prev <- estimatePrevalence(
-  #   cdm = cdm, interval = "years",
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE
-  # )
-  # plot <- plotPrevalence(prev)
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # # with a different x axis
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm,name = "denominator",
-  #   cohortDateRange = c(as.Date("2010-01-01"), as.Date("2010-06-01")),
-  #   ageGroup = list(
-  #     c(0, 30),
-  #     c(31, 100)
-  #   )
-  # )
-  # prev <- estimatePrevalence(
-  #   cdm = cdm, interval = "years",
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome", minCellCount = 0
-  # )
-  # plot <- plotPrevalence(prev,
-  #                        x = "denominator_age_group"
-  # )
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # CDMConnector::cdm_disconnect(cdm)
-})
-
-test_that("plot facets", {
-  # skip_if_not_installed("ggplot2")
-  # skip_if_not_installed("scales")
-  # cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm,name = "denominator",
-  #   ageGroup = list(
-  #     c(0, 30),
-  #     c(31, 100)
-  #   )
-  # )
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome", minCellCount = 0
-  # )
-  # plot_orig <- plotIncidence(inc, facet = "denominator_age_group")
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE, minCellCount = 0
-  # )
-  # plot_sr <- plotIncidence(inc, facet = "denominator_age_group")
-  #
-  # expect_true(ggplot2::is.ggplot(plot_sr))
-  #
-  #
-  #
-  # # multiple facet grouping
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm,name = "denominator",
-  #   ageGroup = list(
-  #     c(0, 30),
-  #     c(31, 100)
-  #   ),
-  #   sex = c("Male", "Female")
-  # )
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE
-  # )
-  #
-  # plot <- plotIncidence(inc,
-  #                       facet = c(
-  #                         "denominator_age_group",
-  #                         "denominator_sex"
-  #                       )
-  # )
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # CDMConnector::cdm_disconnect(cdm)
-})
-
-test_that("plot colour", {
-  # skip_if_not_installed("ggplot2")
-  # skip_if_not_installed("scales")
-  # cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm,name = "denominator",
-  #   ageGroup = list(
-  #     c(0, 30),
-  #     c(31, 100)
-  #   )
-  # )
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE
-  # )
-  #
-  # plot <- plotIncidence(inc,
-  #                       colour = "denominator_age_group",
-  #                       colour_name = "Age group"
-  # )
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # # multiple grouping
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm,name = "denominator",
-  #   ageGroup = list(
-  #     c(0, 30),
-  #     c(31, 100)
-  #   ),
-  #   sex = c("Male", "Female")
-  # )
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE
-  # )
-  #
-  # plot <- plotIncidence(inc,
-  #                       colour = c(
-  #                         "denominator_age_group",
-  #                         "denominator_sex"
-  #                       )
-  # )
-  #
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # CDMConnector::cdm_disconnect(cdm)
-})
-
-test_that("plot options", {
-  # skip_if_not_installed("ggplot2")
-  # skip_if_not_installed("scales")
-  # cdm <- mockIncidencePrevalenceRef(sampleSize = 10000)
-  # cdm <- generateDenominatorCohortSet(
-  #   cdm = cdm,
-  #   name = "denominator",
-  #   ageGroup = list(c(0, 30),
-  #                   c(31, 100))
-  # )
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE
-  # )
-  #
-  # plotOptions <- list('hideConfidenceInterval' = TRUE,
-  #                     'facetNcols' = 1)
-  # plot <- plotIncidence(inc,
-  #                       colour = "denominator_age_group",
-  #                       colour_name = "Age group",
-  #                       options = plotOptions)
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # # prevalence
-  # prev <- estimatePrevalence(
-  #   cdm = cdm, interval = "years",
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   minCellCount = 0
-  # )
-  #
-  # plot <- plotPrevalence(prev,
-  #                        colour = c("denominator_age_group",
-  #                                   "denominator_sex"),
-  #                        options = plotOptions)
-  #
-  # expect_true(ggplot2::is.ggplot(plot))
-  #
-  # CDMConnector::cdm_disconnect(cdm)
-})
-
-
-# original result format
-
 test_that("basic incidence plot", {
   skip_on_cran()
   skip_if_not_installed("ggplot2")
   skip_if_not_installed("scales")
-  cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
+
+  cdm <- mockIncidencePrevalenceRef(sampleSize = 100)
   cdm <- generateDenominatorCohortSet(
     cdm = cdm, name = "denominator",
     cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
@@ -263,7 +33,7 @@ test_that("basic incidence plot", {
   expect_true(ggplot2::is.ggplot(plot))
 
   CDMConnector::cdm_disconnect(cdm)
-  })
+})
 
 test_that("basic prevalence plot", {
   skip_on_cran()
@@ -297,7 +67,7 @@ test_that("basic prevalence plot", {
     outcomeTable = "outcome", minCellCount = 0
   )
   plot <- plotPrevalence(prev,
-    x = "denominator_age_group"
+                         x = "denominator_age_group"
   )
   expect_true(ggplot2::is.ggplot(plot))
 
@@ -316,14 +86,22 @@ test_that("plot facets", {
       c(31, 100)
     )
   )
+  inc <- estimateIncidence(
+    cdm = cdm,
+    denominatorTable = "denominator",
+    outcomeTable = "outcome", minCellCount = 0
+  )
+  plot_orig <- plotIncidence(inc, facet = "denominator_age_group")
   inc <- estimateIncidence(
     cdm = cdm,
     denominatorTable = "denominator",
     outcomeTable = "outcome"
   )
+  plot_sr <- plotIncidence(inc, facet = "denominator_age_group")
+
+  expect_true(ggplot2::is.ggplot(plot_sr))
+
 
-  plot <- plotIncidence(inc, facet = "denominator_age_group")
-  expect_true(ggplot2::is.ggplot(plot))
 
   # multiple facet grouping
   cdm <- generateDenominatorCohortSet(
@@ -341,10 +119,10 @@ test_that("plot facets", {
   )
 
   plot <- plotIncidence(inc,
-    facet = c(
-      "denominator_age_group",
-      "denominator_sex"
-    )
+                        facet = c(
+                          "denominator_age_group",
+                          "denominator_sex"
+                        )
   )
   expect_true(ggplot2::is.ggplot(plot))
 
@@ -370,8 +148,8 @@ test_that("plot colour", {
   )
 
   plot <- plotIncidence(inc,
-    colour = "denominator_age_group",
-    colour_name = "Age group"
+                        colour = "denominator_age_group",
+                        colour_name = "Age group"
   )
   expect_true(ggplot2::is.ggplot(plot))
 
@@ -391,10 +169,10 @@ test_that("plot colour", {
   )
 
   plot <- plotIncidence(inc,
-    colour = c(
-      "denominator_age_group",
-      "denominator_sex"
-    )
+                        colour = c(
+                          "denominator_age_group",
+                          "denominator_sex"
+                        )
   )
 
   expect_true(ggplot2::is.ggplot(plot))
@@ -437,7 +215,7 @@ test_that("plot options", {
 
   plot <- plotPrevalence(prev,
                          colour = c("denominator_age_group",
-                                   "denominator_sex"),
+                                    "denominator_sex"),
                          options = plotOptions)
 
   expect_true(ggplot2::is.ggplot(plot))
@@ -445,3 +223,4 @@ test_that("plot options", {
   CDMConnector::cdm_disconnect(cdm)
 })
 
+
diff --git a/tests/testthat/test-tables.R b/tests/testthat/test-tables.R
index 15f2819..255a8b2 100644
--- a/tests/testthat/test-tables.R
+++ b/tests/testthat/test-tables.R
@@ -1,118 +1,40 @@
 test_that("test tables", {
-  # cdm <- mockIncidencePrevalenceRef()
-  #
-  # cdm <- generateDenominatorCohortSet(cdm = cdm, name = "denominator")
-  #
-  # prev_period <- estimatePeriodPrevalence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE
-  # )
-  #
-  # gt1 <- tablePrevalence(prev_period, prevalenceType = "period",
-  #                        header = c("group", "strata"),
-  #                        denominatorSettings = FALSE)
-  # expect_true("gt_tbl" %in% class(gt1))
-  # expect_true(all(colnames(gt1$`_data`) == c(
-  #   'Database name', 'Outcome cohort name', 'Estimate name', 'Prevalence start date', 'Prevalence end date', '[header]Denominator cohort name\n[header_level]Denominator cohort 1'
-  # )))
-  # expect_true(all(unique(gt1$`_data`$`Estimate name`) == c(
-  #   "Denominator (N)", "Outcome (N)", "Prevalence [95% CI]"
-  # )))
-  #
-  # # denominator name false, outcome settings true
-  # expect_message(
-  #   tib1 <- tablePrevalence(
-  #     prev_period,
-  #     prevalenceType = "period",
-  #     type = "tibble",
-  #     denominatorName = FALSE,
-  #     outcomeSettings = TRUE,
-  #     header = c("group", "strata"),
-  #     denominatorSettings = FALSE
-  #   )
-  # )
-  # expect_true(all(colnames(tib1) == c('Database name', 'Outcome cohort name', 'Estimate name', 'Estimate value', 'Prevalence start date', 'Prevalence end date')))
-  #
-  # # no split strata
-  # fx <- tablePrevalence(
-  #   prev_period,
-  #   prevalenceType = "period",
-  #   denominatorName = FALSE,
-  #   analysisSettings = TRUE,
-  #   outcomeName = FALSE,
-  #   header = "cdm_name",
-  #   splitStrata = FALSE,
-  #   type = "tibble",
-  #   denominatorSettings = FALSE
-  # )
-  # expect_true(all(colnames(fx$body$dataset) == c(
-  #   'Strata name', 'Strata level', 'Estimate name', 'Prevalence start date', 'Prevalence end date', 'Analysis type',
-  #   'Analysis interval', 'Analysis complete database intervals', 'Analysis full contribution', 'Database name\nmock'
-  # )))
-  #
-  # # estimate in header
-  # fx2 <- tablePrevalence(
-  #   prev_period,
-  #   header = c("strata", "estimate"),
-  #   splitStrata = TRUE,
-  #   type = "flextable",
-  #   prevalenceType = "period",
-  #   denominatorSettings = FALSE
-  # )
-  # expect_true("flextable" %in% class(fx2))
-  # expect_true(all(colnames(fx2$body$dataset) == c(
-  #   'Database name', 'Denominator cohort name', 'Outcome cohort name', 'Prevalence start date', 'Prevalence end date', 'Denominator (N)', 'Outcome (N)', 'Prevalence [95% CI]'
-  # )))
-  #
-  # # point prevalence
-  # prev_point <- estimatePointPrevalence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   summarisedResult = TRUE
-  # )
-  # gt2 <- tablePrevalence(prev_point, denominatorSettings = TRUE, prevalenceType = "point")
-  # expect_true(all(colnames(gt2$`_data`) == c(
-  #   'Database name', 'Denominator cohort name', 'Prevalence start date', 'Prevalence end date',
-  #   'Denominator age group', 'Denominator sex', 'Denominator days prior observation', 'Denominator start date',
-  #   'Denominator end date', 'Denominator target cohort name',
-  #   '[header_level]Outcome cohort name\n[header_level]Cohort 1\n[header_level]Denominator (N)',
-  #   '[header_level]Outcome cohort name\n[header_level]Cohort 1\n[header_level]Outcome (N)',
-  #   '[header_level]Outcome cohort name\n[header_level]Cohort 1\n[header_level]Prevalence [95% CI]'
-  # )))
-  #
-  # # incidence
-  # cdm$denominator <- cdm$denominator %>%
-  #   dplyr::mutate(my_strata = dplyr::if_else(year(cohort_start_date) < 1995,
-  #                                            "first", "second")) %>%
-  #   dplyr::compute()
-  # inc <- estimateIncidence(
-  #   cdm = cdm,
-  #   denominatorTable = "denominator",
-  #   outcomeTable = "outcome",
-  #   interval = "months",
-  #   strata = list(c("my_strata")),
-  #   summarisedResult = TRUE
-  # )
-  # tableInc <- tableIncidence(inc,
-  #                            outcomeName = FALSE,
-  #                            outcomeSettings = FALSE,
-  #                            type = "flextable",
-  #                            header = c("group", "strata"),
-  #                            denominatorSettings = FALSE)
-  # expect_true("flextable" %in% class(tableInc))
-  # expect_true(all(colnames(tableInc$body$dataset) == c(
-  #   'Database name', 'Estimate name', 'Incidence start date', 'Incidence end date',
-  #   'Denominator cohort name\nDenominator cohort 1\nMy strata\nOverall',
-  #   'Denominator cohort name\nDenominator cohort 1\nMy strata\nFirst'
-  # )))
-  #
-  #
-  # # no split strata
-  # expect_no_error(tableIncidence(inc, outcomeName = FALSE,
-  #                                outcomeSettings = TRUE, type = "flextable"))
-  #
-  # CDMConnector::cdm_disconnect(cdm)
+  cdm <- mockIncidencePrevalenceRef()
+
+  cdm <- generateDenominatorCohortSet(cdm = cdm, name = "denominator")
+
+  prev_period <- estimatePeriodPrevalence(
+    cdm = cdm,
+    denominatorTable = "denominator",
+    outcomeTable = "outcome"
+  )
+
+  gt1 <- tablePrevalence(prev_period, type = "gt")
+  expect_true("gt_tbl" %in% class(gt1))
+
+  # point prevalence
+  prev_point <- estimatePointPrevalence(
+    cdm = cdm,
+    denominatorTable = "denominator",
+    outcomeTable = "outcome"
+  )
+  expect_no_error(gt2 <- tablePrevalence(prev_point))
+
+  # incidence
+  cdm$denominator <- cdm$denominator %>%
+    dplyr::mutate(my_strata = dplyr::if_else(year(cohort_start_date) < 1995,
+                                             "first", "second")) %>%
+    dplyr::compute()
+  inc <- estimateIncidence(
+    cdm = cdm,
+    denominatorTable = "denominator",
+    outcomeTable = "outcome",
+    interval = "months",
+    strata = list(c("my_strata"))
+  )
+  tableInc <- tableIncidence(inc,
+                             type = "flextable")
+  expect_true("flextable" %in% class(tableInc))
+
+  CDMConnector::cdm_disconnect(cdm)
 })