Abstract
BackgroundObservational post-marketing assessment studies often involve evaluating the effect of a rare treatment on a time-to-event outcome, through the estimation of a marginal hazard ratio. Propensity score (PS) methods are the most used methods to estimate marginal effect of an exposure in observational studies. However there is paucity of data concerning their performance in a context of low prevalence of exposure.MethodsWe conducted an extensive series of Monte Carlo simulations to examine the performance of the two preferred PS methods, known as PS-matching and PS-weighting to estimate marginal hazard ratios, through various scenarios.ResultsWe found that both PS-weighting and PS-matching could be biased when estimating the marginal effect of rare exposure. The less biased results were obtained with estimators of average treatment effect in the treated population (ATT), in comparison with estimators of average treatment effect in the overall population (ATE). Among ATT estimators, PS-weighting using ATT weights outperformed PS-matching. These results are illustrated using a real observational study.ConclusionsWhen clinical objectives are focused on the treated population, applied researchers are encouraged to estimate ATT with PS-weighting for studying the relative effect of a rare treatment on time-to-event outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0135-1) contains supplementary material, which is available to authorized users.
Highlights
Observational post-marketing assessment studies often involve evaluating the effect of a rare treatment on a time-to-event outcome, through the estimation of a marginal hazard ratio
When sample size increased (y = 30, approximatively 1100 subjects overall), propensity score weighting (PSW)-average treatment effect (ATE) and Propensity score (PS)-matching showed very similar results, and PSW-average treatment effect in treated subjects (ATT) was still the best method according to bias and coverage performance parameters
Variability ratios increased with the sample size, and became clearly larger than 1 for PS-weighting using ATT weights (PSW-ATT) method
Summary
Observational post-marketing assessment studies often involve evaluating the effect of a rare treatment on a time-to-event outcome, through the estimation of a marginal hazard ratio. Observational studies may end up with an imbalance of baseline characteristics between exposed and unexposed subjects If some of these characteristics are associated with the outcome of interest, we. Among the methods used to account for confounding factors in observational studies, propensity score (PS) analysis has been increasingly used [7]. PS analysis was developed to take into account the problem of confounding in observational studies [8], inducing baseline balance of measured confounding factors between groups of exposed and unexposed subjects. The first step corresponds to the estimation of the probability of exposure conditional on baseline confounding factors.
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