Estimation of treatment effects in randomized clinical trials which involve non-trial departures

Restricted until 23 Apr. 2010.Motivated by a real clinical trial, we consider the problem of estimating the causal treatment effect of a two-arm randomized controlled trial in which some of the participants selected a third treatment outside of the protocol. Following Rubin's potential outcome model approach, we classified the study sample into nine subgroups according to their potential compliance behaviors under each assignment. Under two alternative sets of assumptions outlined, a relative risk estimator for a binary outcome is proposed and estimated for the subgroups of participants identified as providing information to the causal estimation. Asymptotic performance of the proposed estimator is evaluated both theoretically and through simulation. Our approach is compared with traditional approaches including intent-to-treat, per-protocol, as- treated and instrumental variable analyses. Results show our proposed estimator is asymptotically unbiased and thus gives a better estimate of the true treatment effect for the subpopulation than other estimators. Illustration of application to a real dataset is also presented