Inverse probability of treatment weighted Kaplan-Meier estimates have been developed to compare two treatments in the presence of confounders in observational studies. Recently, stabilized weights were developed to reduce the influence of extreme inverse probability of treatment-weighted weights in estimating treatment effects. The objective of this research was to use adjusted Kaplan-Meier estimates and modified log-rank and Wilcoxon tests to examine the effect of a treatment that varies over time in an observational study.
We proposed stabilized weight adjusted Kaplan-Meier estimates and modified log-rank and Wilcoxon tests when the treatment was time-varying over the follow-up period. We applied these new methods in examining the effect of an anti-platelet agent, clopidogrel, on subsequent events, including bleeding, myocardial infarction, and death after a drug-eluting stent was implanted into a coronary artery. In this population, clopidogrel use may change over time based on a patient's behavior (e.g., nonadherence) and physicians' recommendations (e.g., end of duration of therapy). Consequently, clopidogrel use was treated as a time-varying variable.
We demonstrate that 1) the sample sizes at three chosen time points are almost identical in the original and weighted datasets; and 2) the covariates between patients on and off clopidogrel were well balanced after stabilized weights were applied to the original samples.
The stabilized weight-adjusted Kaplan-Meier estimates and modified log-rank and Wilcoxon tests are useful in presenting and comparing survival functions for time-varying treatments in observational studies while adjusting for known confounders.