Blinded assessment of treatment effects for survival endpoint in an ongoing trial

Pharmaceutical Statistics
Jun XieJi Zhang

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 ...Continue Reading

References

Feb 17, 1998·Statistics in Medicine·A L Gould, W J Shih
Jan 10, 2002·Statistics in Medicine·Tim Friede, Meinhard Kieser
Apr 2, 2005·Statistics in Medicine·Biao Xing, Jitendra Ganju
Nov 30, 2006·Pharmaceutical Statistics·Joel A Waksman

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