Some thoughts on sample size : A Bayesian-frequentist hybrid approach
Some thoughts on sample size : A Bayesian-frequentist hybrid approach
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DOI：
10.1177/1740774512453784
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发表时间：
2012-10-01
期刊：
影响因子：
2.7
通讯作者：
Wittes, Janet T.
中科院分区：
文献类型：
--
作者：
Lan, K. K. Gordan;Wittes, Janet T.
传统的样本量计算没有正式纳入可能效应量的不确定性。使用一个正常的前表示的不确定性，最近建议，可以导致功率不接近1作为样本大小接近infinites.Purpose提供方法计算样本大小和功率，正式纳入不确定性的影响大小。有关公式应确保功率接近一个样本量无限增加，应该很容易calculation.Methods我们检查正常，截断正常，伽玛先验的影响大小计算和演示分析的方法来近似的功率截断正常前。我们还提出了一个简单的折衷方法，需要一个适度更大的样本量比一个来自固定效应的方法。结果使用一个现实的先验分布，而不是一个固定的治疗效果可能会增加3期试验所需的样本量。从II期试验中获得的效应量估计值移动到III期试验样本量的标准固定效应方法忽略了II期试验估计值的固有变异性。截断的正常先验似乎需要不切实际的大样本量，而伽玛先验似乎把太多的概率大的影响大小，因此产生不切实际的高power.Limitations-文章处理了几个例子和有限的参数范围。它不明确处理与二进制或故障时间data.Conclusions使用的标准固定的方法来计算样本量往往会产生一个样本量导致较低的功率比预期的。其他自然参数先验导致不可接受的大样本量或不切实际的高功率。我们推荐一种方法，该方法是假设固定效应量和在效应量之前分配正常值之间的折衷。临床试验2012; 9：561-569。http://ctj.sagepub.com
Background Traditional calculations of sample size do not formally incorporate uncertainty about the likely effect size. Use of a normal prior to express that uncertainty, as recently recommended, can lead to power that does not approach 1 as the sample size approaches infinity.Purpose To provide approaches for calculating sample size and power that formally incorporate uncertainty about effect size. The relevant formulas should ensure that power approaches one as sample size increases indefinitely and should be easy to calculate.Methods We examine normal, truncated normal, and gamma priors for effect size computationally and demonstrate analytically an approach to approximating the power for a truncated normal prior. We also propose a simple compromise method that requires a moderately larger sample size than the one derived from the fixed effect method.Results Use of a realistic prior distribution instead of a fixed treatment effect is likely to increase the sample size required for a Phase 3 trial. The standard fixed effect method for moving from estimates of effect size obtained in a Phase 2 trial to the sample size of a Phase 3 trial ignores the variability inherent in the estimate from Phase 2. Truncated normal priors appear to require unrealistically large sample sizes while gamma priors appear to place too much probability on large effect sizes and therefore produce unrealistically high power.Limitations The article deals with a few examples and a limited range of parameters. It does not deal explicitly with binary or time-to-failure data.Conclusions Use of the standard fixed approach to sample size calculation often yields a sample size leading to lower power than desired. Other natural parametric priors lead either to unacceptably large sample sizes or to unrealistically high power. We recommend an approach that is a compromise between assuming a fixed effect size and assigning a normal prior to the effect size. Clinical Trials 2012; 9 : 561-569. http://ctj.sagepub.com