Addressing Post-Treatment Selection Bias in Comparative Effectiveness Research, Using Real-world Data and Simulation.

To examine methodologies that address imbalanced treatment switching and censoring, six different analytic approaches were evaluated under a comparative effectiveness framework: intention-to-treat, as-treated, intention-to-treat with censor-weighting, as-treated with censor-weighting, time-varying exposure, and time-varying exposure with censor-weighting. Marginal structural models were employed to address time-varying exposure, confounding, and possibly informative censoring in an administrative dataset of adult patients who were hospitalized with acute coronary syndrome and treated with either clopidogrel or ticagrelor. The effectiveness endpoint included first occurrence of death, myocardial infarction, or stroke. These methodologies were then applied across simulated datasets with varying frequencies of treatment switching and censoring to compare the effect estimate of each analysis. The findings suggest that implementing different analytic approaches impacts the point estimate and interpretation of analyses, especially when censoring is highly unbalanced.