Methods for designing a comparable replication study have received considerable attention in the published literature, with both Bayesian and non-Bayesian methods having been developed from a hypothesis testing and associated P-value perspective. The purpose of this paper is to describe, using a maximum likelihood-based confidence interval framework, a new frequentist method for choosing the sample size for a comparable replication study. This new method is compared to the published “predictive power” (or “PP”) method. For each of these two methods, a new and easy-to-use formula is derived for computing the optimal comparable replication study sample size that guarantees satisfying a specific confidence interval criterion with a chosen high minimum probability. Connections to hypothesis testing are made, and the Discussion section provides further commentary and considers a numerical example involving published data.

设计可比较的复制研究的方法在已发表的文献中受到了相当大的关注，贝叶斯和非贝叶斯方法都是从假设检验和相关 P 值的角度开发的。本文的目的是使用基于最大似然的置信区间框架来描述一种新的频率论方法，用于为可比较的复制研究选择样本量。这种新方法与已发布的“预测能力”（或“PP”）方法进行了比较。对于这两种方法中的每一种，都导出了一个新的且易于使用的公式，用于计算最佳可比复制研究样本量，该样本量保证以选定的高最小概率满足特定的置信区间标准。与假设检验建立联系，