Craig edits

Characterization.Rmd

@@ -68,7 +68,7 @@ $$
 Incidence\;Rate = \frac{\#\;persons\;in\;cohort\;with\;new\;outcome\;during\;TAR}{person\;time\;at\;risk\;contributed\;by\;persons\;in\;cohort}
 $$
 
-An incidence rate is a mesure of the number of new outcomes during the cumulative TAR for the population. When a person experiences the outcome in the TAR, their contribution to the total person-time stops at the occurrence of the outcome event. The cumulative TAR is referred to as **person-time** and is expressed in days, months or years.\index{incidence!rate} \index{person-time}
+An incidence rate is a measure of the number of new outcomes during the cumulative TAR for the population. When a person experiences the outcome in the TAR, their contribution to the total person-time stops at the occurrence of the outcome event. The cumulative TAR is referred to as **person-time** and is expressed in days, months or years.\index{incidence!rate} \index{person-time}
 
 When calculated for therapies, incidence proportions and incidence rates of use of a given therapy are classic population-level DUS.
 
@@ -273,7 +273,7 @@ settings <- createCovariateSettings(useConditionEraLongTerm = TRUE,
                                     endDays = -1) 
 ```
 
-This redefines the long term window as 180 days prior up to (but not including) the cohort start date, and redefines the short term window as 14 days prior up to (but not including) the cohort start date. 
+This redefines the long-term window as 180 days prior up to (but not including) the cohort start date, and redefines the short term window as 14 days prior up to (but not including) the cohort start date. 
 
 Again, we can also specify which concept IDs should or should not be used to construct covariates: 
 
@@ -348,7 +348,7 @@ knitr::include_graphics("images/Characterization/pathwaysPersonEventView.png")
 
 In figure \@ref(fig:pathwaysPersonEventView), the person is part of the target cohort with a defined start and end date. Then, the numbered line segments represent where that person also is identified in an event cohort for a duration of time. Event cohorts allow us to describe any clinical event of interest that is represented in the CDM such that we are not constrained to creating a pathway for a single domain or concept. 
 
-To start, click on ![](images/Characterization/atlasPathwaysMenuItem.png) in the left bar of ATLAS to create a new cohort pathways study. Provide a descriptive name and press the save button . 
+To start, click on ![](images/Characterization/atlasPathwaysMenuItem.png) in the left bar of ATLAS to create a new cohort pathways study. Provide a descriptive name and press the save button. 
 
 ### Design
 
@@ -477,7 +477,7 @@ As shown, the metrics calculated are the same but the interpretation is differen
 
 #### Prerequisites {-}
 
-For these exercise, access to an ATLAS instance is required. You can use the instance at [http://atlas-demo.ohdsi.org](http://atlas-demo.ohdsi.org), or any other instance you have acces to. 
+For these exercises, access to an ATLAS instance is required. You can use the instance at [http://atlas-demo.ohdsi.org](http://atlas-demo.ohdsi.org), or any other instance you have acces to. 
 
 ```{exercise, exerciseCharacterization1}
 We would like to understand how celecoxib is used in the real world. To start, we would like to understand what data a database has on this drug. Use the ATLAS Data Sources module to find information on celecoxib.

Cohorts.Rmd

@@ -94,7 +94,7 @@ Table: (\#tab:conceptSetExpression) An example concept set expression.
 
 As shown in Figure \@ref(fig:conceptSet), this will include "Myocardial infarction" and all of its descendants except "Old myocardial infarction" and its descendants. In total, this concept set expression implies nearly a hundred Standard Concepts. These Standard Concepts in turn reflect hundreds of source codes (e.g. ICD-9 and ICD-10 codes) that may appear in the various databases. 
 
-```{r conceptSet, fig.cap='A concept set including "Myocardial infaction (with descendants), but excluding "Old myocardial infarction" (with descendants).',echo=FALSE, out.width='100%', fig.align='center'}
+```{r conceptSet, fig.cap='A concept set including "Myocardial infaction" (with descendants), but excluding "Old myocardial infarction" (with descendants).',echo=FALSE, out.width='100%', fig.align='center'}
 knitr::include_graphics("images/Cohorts/conceptSet.png")
 ```
 
@@ -110,7 +110,7 @@ An example of applying this approach on data in the CDM is the APHRODITE (Automa
 
 When you are building a cohort, you should consider which of these is more important to you: *finding all the eligible patients?* versus *Getting only the ones you are confident about?*
 
-Your strategy to construct your cohort will depend on your the clinical stringency of how your expert consensus defines the disease. This is to say, the right cohort design will depend on the question you’re trying to answer. You may opt to build a cohort definition that uses everything you can get, uses the lowest common denominator so you can share it across OHDSI sites or is a compromise of the two. It is ultimately at the researcher’s discretion what threshold of stringency is necessary to adequately study the cohort of interest.
+Your strategy to construct your cohort will depend on the clinical stringency of how your expert consensus defines the disease. This is to say, the right cohort design will depend on the question you’re trying to answer. You may opt to build a cohort definition that uses everything you can get, uses the lowest common denominator so you can share it across OHDSI sites or is a compromise of the two. It is ultimately at the researcher’s discretion what threshold of stringency is necessary to adequately study the cohort of interest.
 
 As mentioned at the beginning of the chapter, a cohort definition is an attempt to infer something we would like to observe from the data that is recorded. This begs the question how well we succeeded in that attempt. In general, the validation of a rule-based cohort definition or probabilistic algorithm can be thought of as a test of the proposed cohort compared to some form of “gold standard” reference (e.g. manual chart review of cases). This is discussed in detail in Chapter \@ref(ClinicalValidity) ("Clinical Validity").
 
@@ -195,7 +195,7 @@ knitr::include_graphics("images/Cohorts/ATLAS-findingyourconcept.png")
 
 ### Additional Initial Event Criteria
 
-Now that you've attached a concept set, you are not done yet. Your question is looking for new users or the first time in someone's history they are exposed to ACE inhibitors. This translate to the *first exposure* of ACE inhibitors in the patient's record. To specify this, you need to click "+Add attribute". You will want to select the "Add first exposure criteria". Notice, you could specify other attributes of a criteria you build. You could specify an attribute of age at occurrence, the date of occurrence, gender or other attributes related to the drug. Criteria available for selection will look different for each domain.
+Now that you've attached a concept set, you are not done yet. Your question is looking for new users or the first time in someone's history they are exposed to ACE inhibitors. This translates to the *first exposure* of ACE inhibitors in the patient's record. To specify this, you need to click "+Add attribute". You will want to select the "Add first exposure criteria". Notice, you could specify other attributes of a criteria you build. You could specify an attribute of age at occurrence, the date of occurrence, gender or other attributes related to the drug. Criteria available for selection will look different for each domain.
 
 From there, the window will automatically close. Once selected, this additional attribute will show up in the same box as the initial criteria (see Figure \@ref(fig:initialEventAce)).
 
@@ -204,7 +204,7 @@ The current design of ATLAS may confuse some. Despite its appearance, the ![](im
 
 ```
 
-Now you have built an initial qualifying event. To ensure you are capturing the first observed drug exposure, you will want to add a look-back window to know that you are looking at enough of the patient's history to know what comes first. It is possible that a patient with a short observation period may have received an exposure elsewhere that we do not see. We cannot control this but we can mandate a minimum amount of time the patient must be in the data prior to the index date You can do this by adjusting the continuous observation drop downs. You could also click the box and type in a value to these windows. We will require 365 days of of continuous observation prior to the initial event. You will update your observation period to: *with continuous observation of 365 days before*, as shown in Figure \@ref(fig:initialEventAce). This look-back window is the discretion of your study team. You may choose differently in other cohorts. This creates, as best as we are able, a minimum period of time we see the patient to ensure we are capturing the first record. This criteria is about prior history and does not involve time after the index event. Therefore, we require 0 days after the index event. Our qualifying event is the first-ever use of ACE inhibitors. Thus, we limit initial events to the "earliest event" per person.
+Now you have built an initial qualifying event. To ensure you are capturing the first observed drug exposure, you will want to add a look-back window to know that you are looking at enough of the patient's history to know what comes first. It is possible that a patient with a short observation period may have received an exposure elsewhere that we do not see. We cannot control this but we can mandate a minimum amount of time the patient must be in the data prior to the index date You can do this by adjusting the continuous observation drop downs. You could also click the box and type in a value to these windows. We will require 365 days of continuous observation prior to the initial event. You will update your observation period to: *with continuous observation of 365 days before*, as shown in Figure \@ref(fig:initialEventAce). This look-back window is the discretion of your study team. You may choose differently in other cohorts. This creates, as best as we are able, a minimum period of time we see the patient to ensure we are capturing the first record. This criteria is about prior history and does not involve time after the index event. Therefore, we require 0 days after the index event. Our qualifying event is the first-ever use of ACE inhibitors. Thus, we limit initial events to the "earliest event" per person.
 
 ```{r initialEventAce, fig.cap='Setting the required continuous observation before the index date.',echo=FALSE, out.width='100%', fig.align='center'}
 knitr::include_graphics("images/Cohorts/initialEventAce.png")
@@ -216,7 +216,7 @@ To further explain how this logic comes together, you can think about assembling
 knitr::include_graphics("images/Cohorts/EarliestEventExplained.png")
 ```
 
-In Figure \@ref(fig:EarliestEventExplained), each line represents a single patient that may be eligible to join the cohort. The filled in stars represent a time the patient fulfills the specified criteria. As additional criteria is applied, you may see some stars are a lighter shade. This means that these patients have other records that satisfy the criteria but there is another record that proceeds that. By the time we get to the last criteria, we are looking at the cumulative view of patients who have ACE inhibitors for the first time and have 365 days prior to the first time occurrence. Logically, limiting to the initial event is redundant though it is helpful to maintain our explicit logic in every selection we make. When you are building your own cohorts, you may opt to engage the Researchers section of the [OHDSI Forum](http://forums.ohdsi.org) to get a second opinion on how to construct your cohort logic.
+In Figure \@ref(fig:EarliestEventExplained), each line represents a single patient that may be eligible to join the cohort. The filled in stars represent a time the patient fulfills the specified criteria. As additional criteria is applied, you may see some stars are a lighter shade. This means that these patients have other records that satisfy the criteria but there is another record that proceeds that. By the time we get to the last criteria, we are looking at the cumulative view of patients who have ACE inhibitors for the first time and have 365 days prior to the first-time occurrence. Logically, limiting to the initial event is redundant though it is helpful to maintain our explicit logic in every selection we make. When you are building your own cohorts, you may opt to engage the Researchers section of the [OHDSI Forum](http://forums.ohdsi.org) to get a second opinion on how to construct your cohort logic.
 
 ### Inclusion Criteria
 
@@ -604,7 +604,7 @@ Use ATLAS to create a cohort definition following these criteria:
 - Ages 16 or older
 - With at least 365 days of continuous observation prior to exposure
 - Without prior exposure to any NSAID (Non-Steroidal Anti-Inflammatory Drug)
-- Without prior diagnose of cancer
+- Without prior diagnosis of cancer
 - With cohort exit defined as discontinuation of exposure (allowing for a 30-day gap)
 
 ```

CommonDataModel.Rmd

@@ -8,7 +8,7 @@ Observational data provides a view of what happens to a patient while receiving
 
 Why do we need a Common Data Model for observational healthcare data? 
 
-Depending on their primary needs none of the observational databases capture all clinical events equally well. Therefore, research results must be drawn from many disparate data sources and compared and contrasted to understand the effect of potential capture bias. In addition, in order to draw conclusions with statistical power we need large numbers of observed patients. That explains the need for assessing and analyzing multiple data sources concurrently. In order to do that, data need to be harmonized into a common data standard. In addition, patient data require a high level of protection. To extract data for analysis purposes as it is done traditionally requires strict data use agreements and complex access control. A common data standard can eleviate this need by omitting the extraction step and allowing a standardized analytic to be executed on the data in it's native environment - the analytic comes to the data instead of the data to the analytic.
+Depending on their primary needs none of the observational databases capture all clinical events equally well. Therefore, research results must be drawn from many disparate data sources and compared and contrasted to understand the effect of potential capture bias. In addition, in order to draw conclusions with statistical power we need large numbers of observed patients. That explains the need for assessing and analyzing multiple data sources concurrently. In order to do that, data need to be harmonized into a common data standard. In addition, patient data require a high level of protection. To extract data for analysis purposes as it is done traditionally requires strict data use agreements and complex access control. A common data standard can alleviate this need by omitting the extraction step and allowing a standardized analytic to be executed on the data in it's native environment - the analytic comes to the data instead of the data to the analytic.
 
 This standard is provided by the Common Data Model (CDM). The CDM, combined with its standardized content (see Chapter \@ref(StandardizedVocabularies)), will ensure that research methods can be systematically applied to produce meaningfully comparable and reproducible results. In this chapter we provide an overview of the data model itself, design, conventions, and discussion of select tables. 
 
@@ -183,7 +183,7 @@ Column name|Value|Explanation
 
 ### OBSERVATION_PERIOD Table{#observationPeriod}
 
-The OBSERVATION_PERIOD table is designed to define the amount of time for which at least a patient's demographics, conditions, procedures and drugs are recorded in the source system with the expection of a reasonable sensitivity and specificity. For insurance data this is typically the enrollment period of the patient. It's trickier in electronic health records (EHR), as most healthcare systems do not determine which healthcare institution or provider is visited. As a next best solution, often the first record in the system is considered the Start Date of the Observation Period and the latest record is considered the End Date.
+The OBSERVATION_PERIOD table is designed to define the amount of time for which at least a patient's demographics, conditions, procedures and drugs are recorded in the source system with the expectation of a reasonable sensitivity and specificity. For insurance data this is typically the enrollment period of the patient. It's trickier in electronic health records (EHR), as most healthcare systems do not determine which healthcare institution or provider is visited. As a next best solution, often the first record in the system is considered the Start Date of the Observation Period and the latest record is considered the End Date.
 
 #### How Is Lauren's Observation Period Defined? {-}
 
@@ -302,7 +302,7 @@ Column name|Value|Explanation
 |QUANTITY|60|The quantity of drug as recorded in the original prescription or dispensing record.|
 |DAYS_SUPPLY|30|The number of days of supply of the medication as prescribed.|
 |SIG|NULL|The directions ("signetur") on the Drug prescription as recorded in the original prescription or dispensing record (and printed on the container in the US drug prescription system). Signeturs are not yet standardized in the CDM, and provided verbatim.|
-|ROUTE_CONCEPT_ID|4132161|This concept is meant to represent the route of administration  of the Drug the patient was was exposed to. Lauren took her acetaminophen orally so the concept ID [4132161](http://athena.ohdsi.org/search-terms/terms/4132161) ("Oral") is used.|
+|ROUTE_CONCEPT_ID|4132161|This concept is meant to represent the route of administration  of the Drug the patient was exposed to. Lauren took her acetaminophen orally so the concept ID [4132161](http://athena.ohdsi.org/search-terms/terms/4132161) ("Oral") is used.|
 |LOT_NUMBER|NULL|An identifier assigned to a particular quantity or lot of Drug product from the manufacturer. This information is rarely captured.|
 |PROVIDER_ID|NULL|If the drug record has a prescribing Provider listed, the ID for that Provider goes in this field. In that case this contains the PROVIDER_ID from the PROVIDER table.|
 |VISIT_OCCURRENCE_ ID|509|A foreign key to the VISIT_OCCURRENCE table during which the Drug was prescribed.|