CXOwt

This R package was is code to implement weighted conditional logistic regression described here.

Installation

CXOwt is still under development. You can install the latest version from GitHub with:

# install.packages("remotes")
remotes::install_github("lan-k/CXOwt")

Data

Data should be structured with one row per period per person. The data should be ordered so that for each person, the earliest period appears first in the data, the case period appears last.

The following 3 variables are required: Patient ID Binary exposure indicator (0 for unexposed or 1 for exposed) Binary indicator for the outcome

• 0 in control periods
• 1 in case period for cases
• 0 in case period for time controls

The package comes with two example dataframes

• “cases” with cases only
• “casetimecontrols” with cases and time controls

The exposure variable is “ex” and the outcome variable is “Event”.

library(CXOwt)

#case-crossover
data(cases)

head(cases)
#> # A tibble: 6 × 6
#>      Id    ex Event   day   age   sex
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     2     1     0     1    48     0
#> 2     2     1     0     2    48     0
#> 3     2     1     0     3    48     0
#> 4     2     0     0     4    48     0
#> 5     2     0     0     5    48     0
#> 6     2     0     0     6    48     0

tail(cases) # last few rows of data for a case
#> # A tibble: 6 × 6
#>      Id    ex Event   day   age   sex
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1   499     1     0    85    33     1
#> 2   499     1     0    86    33     1
#> 3   499     1     0    87    33     1
#> 4   499     1     0    88    33     1
#> 5   499     1     0    89    33     1
#> 6   499     1     1    90    33     1

#case-time-controls
data(casetimecontrols)

tail(casetimecontrols[casetimecontrols$Id == 2,]) # last few rows of data for a time control
#> # A tibble: 6 × 6
#>      Id    ex Event   day   age   sex
#>   <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     2     0     0    85    44     0
#> 2     2     0     0    86    44     0
#> 3     2     0     0    87    44     0
#> 4     2     0     0    88    44     0
#> 5     2     0     0    89    44     0
#> 6     2     0     0    90    44     0

Using the package

CXOwt contains functions for both case-crossover and case-time-control designs. There are 2 example dataframes, cases and casetimecontrols.

CXO_wt_boot() is the version for case-crossover studies, CXO_tc_wt_boot() for case-time-control studies. Both return the weighted Odds Ratio estimate and bootstrapped 95% confidence intervals.

library(CXOwt)

#case-crossover
data(cases)
cfit.b <- CXO_wt_boot(data=cases, exposure = ex, event = Event, Id=Id, B=500) 
summary(cfit.b)

#case-time-control
data(casetimecontrols)
ctcfit.b <- CXO_tc_wt_boot(data=casetimecontrols, exposure = ex, event = Event, Id=Id, B = 500) 
summary(ctcfit.b)

Alternatively, you can return the weighted conditional logistic regression objects without bootstrapping. However, the standard errors and 95% confidence intervals may not be accurate. The output will be a ‘clogit’ object.

#case-crossover
cfit <- CXO_wt(cases, exposure = ex, event = Event, Id=Id)  
exp(cbind(coef(cfit), confint(cfit)))
#>               2.5 % 97.5 %
#> e 1.429248 1.126288 1.8137

#case-time-control
ctcfit <- CXO_tc_wt(casetimecontrols, exposure = ex, event = Event, Id=Id)   
exp(cbind(coef(ctcfit), confint(ctcfit)))
#>                      2.5 %   97.5 %
#> ex1    0.9828992 0.7144712 1.352176
#> ex_tc1 1.5681218 1.2684209 1.938636

Other functions include

• ‘mhor’ to produce Mantel-Haenszel (MH) Odds Ratios
• ‘SCL_bias’ to estimate the bias in (unweighted) conditional logistic regression compared with MH ORs