复制研究的把握度计算
Power Calculations for Replication Studies
摘要：
再现性危机导致越来越多的重复研究正在进行。重复研究的样本量通常使用基于原始研究的效应估计的条件把握度计算。然而，这种方法并不适合，因为它忽略了原始结果的不确定性。贝叶斯方法用于临床试验，将先验信息纳入功效计算。我们建议，以适应这种方法的复制框架，并促进使用的预测，而不是条件的力量在设计的复制研究。此外，我们描述了如何扩展的方法，连续的临床试验，可以定制复制研究。在中期分析计算的条件和预测能力进行了比较，我们认为，预测能力是一个有用的工具，以决定是否提前停止重复研究。最近一个关于社会科学可复制性的项目被用来说明不同方法的特性。
英文摘要：
The reproducibility crisis has led to an increasing number of replication studies being conducted. Sample sizes for replication studies are often calculated using conditional power based on the effect estimate from the original study. However, this approach is not well suited as it ignores the uncertainty of the original result. Bayesian methods are used in clinical trials to incorporate prior information into power calculations. We propose to adapt this methodology to the replication framework and promote the use of predictive instead of conditional power in the design of replication studies. Moreover, we describe how extensions of the methodology to sequential clinical trials can be tailored to replication studies. Conditional and predictive power calculated at an interim analysis are compared and we argue that predictive power is a useful tool to decide whether to stop a replication study prematurely. A recent project on the replicability of social sciences is used to illustrate the properties of the different methods.
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