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Statistical Inference for Self‐Designing Clinical Trials with a One‐Sided Hypothesis
Author(s) -
Shen Yu,
Fisher Lloyd
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00190.x
Subject(s) - sample size determination , type i and type ii errors , statistic , statistics , interim , test statistic , inference , computer science , statistical hypothesis testing , variance (accounting) , statistical inference , interim analysis , null hypothesis , function (biology) , mathematics , econometrics , clinical trial , medicine , artificial intelligence , accounting , archaeology , pathology , evolutionary biology , biology , business , history
Summary. In the process of monitoring clinical trials, it seems appealing to use the interim findings to determine whether the sample size originally planned will provide adequate power when the alternative hypothesis is true, and to adjust the sample size if necessary. In the present paper, we propose a flexible sequential monitoring method following the work of Fisher (1998), in which the maximum sample size does not have to be specified in advance. The final test statistic is constructed based on a weighted average of the sequentially collected data, where the weight function at each stage is determined by the observed data prior to that stage. Such a weight function is used to maintain the integrity of the variance of the final test statistic so that the overall type I error rate is preserved. Moreover, the weight function plays an implicit role in termination of a trial when a treatment difference exists. Finally, the design allows the trial to be stopped early when the efficacy result is sufficiently negative. Simulation studies confirm the performance of the method.