DISCUSSION
We guided readers through a CCW analysis comparing initiation windows for aspirin and clopidogrel in patients after MI in publicly available synthetic Medicare claims data. We discussed how to describe the complex hypothetical interventions under study, whether to clone those incompatible with their regimens at time 0, and why it is sometimes necessary to include only recent initiators in creating IPCW at the end of the treatment window. Finally, we included code visualizing differences in exposure patterns between regimens to provide helpful context to the underlying exposure patterns as well as an example of how to examine censoring weight performance.
Of course, CCW is not a silver bullet. As mentioned previously, the interventions underlying “start by day X” and “start between day X and Y” regimens vary depending on the initiation patterns within the study population. As a result, interpreting treatment effect estimates or comparing findings across different studies studying the same regimens requires information on when cloned populations initiate (similar to studies of other “natural” interventions).13 Additionally, if predictors of treatment initiation associated with the outcome are unmeasured or omitted from IPCW, estimates will be biased. Researchers must also be concerned about exposure, covariate, and outcome measurement error,25 differential surveillance between individuals,26 model misspecification,27 missing data,26and other common sources of bias in pharmacoepidemiologic studies.28
This paper focused on providing readers with the most important tools for performing, understanding, and replicating relatively straightforward CCW studies contrasting initiation windows. Additional elements such as requiring individuals to remain on treatment after initiation or comparing the effects of initiating different treatment options introduces additional analytic complexity, particularly with respect to assigning weights at the end of the grace period.29 We also contrasted effects of different windows rather than comparing windows to never initiating (though code to analyze a “never initiate” regimen, which essentially expands the code covering the first 30 days of the “30-90 day” regimen to the full 180-day period, is also on GitHub). Models for the probability of remaining uncensored were also fit within clones, rather than within the base cohort; under additional parametric assumptions, fitting models within the base cohort can increase precision.16