Marginal structural models and other analyses allow multiple estimates of treatment effects in randomized clinical trials: Meta-epidemiological analysis.

OBJECTIVES: To determine how marginal structural models (MSMs), which are increasingly used to estimate causal effects, are used in randomized clinical trials (RCTs) and compare their results with those from intention-to-treat (ITT) or other analyses. STUDY DESIGN AND SETTING: We searched PubMed, Scopus, citations of key references, and Clinicaltrials.gov. Eligible RCTs reported clinical effects based on MSMs and at least one other analysis. RESULTS: We included 12 RCTs reporting 138 analyses for 24 clinical questions. In 19/24 (79%), MSM-based and other effect estimates were all in the same direction, 22/22 had overlapping 95% confidence intervals (CIs), and in 19/22 (86%), the MSM effect estimate lay within all 95% CIs of all other effects (in two cases no CIs were reported). For the same clinical question, the largest effect estimate from any analysis was 1.19-fold (median; interquartile range 1.13-1.34) larger than the smallest. All MSM and ITT effect estimates were in the same direction and had overlapping 95% CIs. In 71% (12/17), they also agreed on the presence of statistical significance. MSM-based effect estimates deviated more from the null than those based on ITT (P = 0.18). The effect estimates of both approaches differed 1.12-fold (median; interquartile range 1.02-1.22). CONCLUSIONS: MSMs provided largely similar effect estimates as other available analyses. Nevertheless, some of the differences in effect estimates or statistical significance may become important in clinical decision-making, and the multiple estimates require utmost attention of possible selective reporting bias.