Addressing Posttreatment Selection Bias in Comparative Effectiveness Research, Using Real-World Data and Simulation
Date of Original Version
To examine methodologies that address imbalanced treatment switching and censoring, 6 different analytical 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 data set 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 data sets with varying frequencies of treatment switching and censoring to compare the effect estimate of each analysis. The findings suggest that implementing different analytical approaches has an impact on the point estimate and interpretation of analyses, especially when censoring is highly unbalanced.
American journal of epidemiology
Belviso, Nicholas, Yichi Zhang, Herbert D. Aronow, Richard Wyss, Marilyn Barbour, Stephen Kogut, Oluwadolapo D. Lawal, Si Y. Zhan, Prabhani Kuruppumullage Don, and Xuerong Wen. "Addressing Posttreatment Selection Bias in Comparative Effectiveness Research, Using Real-World Data and Simulation." American journal of epidemiology 191, 2 (2022): 331-340. doi:10.1093/aje/kwab242.