title: Blinded assessment of treatment effects for survival endpoint in an ongoing trial.
abstract: Many assumptions, including assumptions regarding treatment effects, are made at the design stage of a clinical trial for power and sample size calculations. It is desirable to check these assumptions during the trial by using blinded data. Methods for sample size re-estimation based on blinded data analyses have been proposed for normal and binary endpoints. However, there is a debate that no reliable estimate of the treatment effect can be obtained in a typical clinical trial situation. In this paper, we consider the case of a survival endpoint and investigate the feasibility of estimating the treatment effect in an ongoing trial without unblinding. We incorporate information of a surrogate endpoint and investigate three estimation procedures, including a classification method and two expectation-maximization (EM) algorithms. Simulations and a clinical trial example are used to assess the performance of the procedures. Our studies show that the expectation-maximization algorithms highly depend on the initial estimates of the model parameters. Despite utilization of a surrogate endpoint, all three methods have large variations in the treatment effect estimates and hence fail to provide a precise conclusion about the treatment effect.
authors.affiliation: Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA. junxie@stat.purdue.edu.
authors.forename: Jun
authors.initials: J
authors.lastname: Xie
authors.forename: Hui
authors.initials: H
authors.lastname: Quan
authors.forename: Ji
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D023381:Endpoint Determination; D006801:Humans; D015233:Models, Statistical; D018401:Sample Size; D016896:Treatment Outcome