Adjusting for observational secondary treatments in estimating the effects of randomized treatments.
In randomized clinical trials, for example, on cancer patients, it is not uncommon that patients may voluntarily initiate a secondary treatment postrandomization, which needs to be properly adjusted for in estimating the "true" effects of randomized treatments. As an alternative to the approach based on a marginal structural Cox model (MSCM) in Zhang and Wang [(2012). Estimating treatment effects from a randomized trial in the presence of a secondary treatment. Biostatistics 13, 625-636], we propose methods that treat the time to start a secondary treatment as a dependent censoring process, which is handled separately from the usual censoring such as the loss to follow-up. Two estimators are proposed, both based on the idea of inversely weighting by the probability of having not started a secondary treatment yet. The second estimator focuses on improving efficiency of inference by a robust covariate-adjustment that does not require any additional assumptions. The proposed methods are evaluated and compared with the MSCM-based method in terms of bias and variance tradeoff using simulations and application to a cancer clinical trial.
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA. mzhangst@umich.edu
Min
M
Zhang
Yanping
Y
Wang
D056808:Biostatistics; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000188:drug therapy; D064887:Observational Studies as Topic / Q000706:statistics & numerical data; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D016896:Treatment Outcome
Causal inference; Comparative effectiveness; Covariate adjustment; Dependent censoring; Inverse probability weighting; Survival analysis