On choosing the number of interim analyses in clinical trials.
On choosing the number of interim analyses in clinical trials.
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关于临床试验中中期分析数量的选择。
DOI：
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发表时间：
1982
期刊：
影响因子：
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通讯作者：
K. McPherson
中科院分区：
文献类型：
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作者：
K. McPherson
通过对大量随机前瞻性患者系列的研究，可以最有效地检测新治疗方法的微小但重要的治疗效果。这种大规模的临床试验如今已经司空见惯。另一种选择是围绕几个试验进行多年的争论和辩论，每个试验都太小，无法确定地发现合理的差异。此类试验产生模棱两可和相互矛盾的结果，可以根据合理的试验前治疗差异估计值通过把握度计算进行预测。不幸的是，这样的计算往往导致几千个样本的大小。
研究者对治疗效果的估计往往过于乐观（这必然是不确定的），尤其是当样本量要求如此严格时，这并不奇怪。在本文中，概述了一种方法纳入样本量计算的不确定性估计在设计阶段的临床试验。特别是一个正式的计划，以决定有多少中期分析应进行，以满足大型临床试验设计的伦理和务实的要求。虽然论点将是“贝叶斯”，评估和比较的标准将是严格的奈曼-皮尔逊（即显着性检验）。
Small but important therapeutic effects of new treatments can be most efficiently detected through the study of large randomized prospective series of patients. Such large scale clinical trials are nowadays commonplace. The alternative is years of polemic and debate surrounding several trials each too small to detect plausible differences with any certainty. Such trials produce equivocal and contradictory results, which could be predicted from power calculations based upon sensible pre-trial estimates of treatment differences. Unfortunately such calculations often lead to sample sizes of several thousands.
It is not surprising that investigators tend to be over-optimistic in their estimation of treatment effects (which are necessarily uncertain) especially when the sample size requirements are so stark. In this paper a method is outlined for incorporating into the sample size calculations the uncertainty of the estimate made at the design stage of a clinical trial. In particular a formal scheme is described for deciding how many interim analyses should be performed to satisfy ethical and pragmatic requirements of large clinical trial design. Although the argument will be ‘Bayesian’, the criteria for assessment and comparison will be strictly of a Neyman-Pearson (i.e. significance testing) kind.