title: Inference for the median residual life function in sequential multiple assignment randomized trials.
abstract: In survival analysis, median residual lifetime is often used as a summary measure to assess treatment effectiveness; it is not clear, however, how such a quantity could be estimated for a given dynamic treatment regimen using data from sequential randomized clinical trials. We propose a method to estimate a dynamic treatment regimen-specific median residual life (MERL) function from sequential multiple assignment randomized trials. We present the MERL estimator, which is based on inverse probability weighting, as well as, two variance estimates for the MERL estimator. One variance estimate follows from Lunceford, Davidian and Tsiatis' 2002 survival function-based variance estimate and the other uses the sandwich estimator. The MERL estimator is evaluated, and its two variance estimates are compared through simulation studies, showing that the estimator and both variance estimates produce approximately unbiased results in large samples. To demonstrate our methods, the estimator has been applied to data from a sequentially randomized leukemia clinical trial.
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, U.S.A.
authors.forename: Kelley M
authors.initials: KM
authors.lastname: Kidwell
authors.forename: Jin H
authors.initials: JH
authors.lastname: Ko
authors.forename: Abdus S
authors.initials: AS
authors.lastname: Wahed
mesh_terms: D000704:Analysis of Variance; D006801:Humans; D007938:Leukemia / Q000628:therapy; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D013223:Statistics as Topic / Q000379:methods*; D016019:Survival Analysis; D016896:Treatment Outcome
keywords: adaptive treatment strategy; dynamic treatment regimen; inverse probability weighting; median residual life function; non-parametric estimation; sequential randomization