A fundamental part of experimental design is to determine the sample size of a study. However, sparse information about population parameters and effect sizes before data collection renders effective sample size planning challenging. Specifically, sparse information may lead research designs to be based on inaccurate a priori assumptions, causing studies to use resources inefficiently or to produce inconclusive results. Despite its deleterious impact on sample size planning, many prominent methods for experimental design fail to adequately address the challenge of sparse a-priori information. Here we propose a Bayesian Monte Carlo methodology for interim design analyses that allows researchers to analyze and adapt their sampling plans throughout the course of a study. At any point in time, the methodology uses the best available knowledge about parameters to make projections about expected evidence trajectories. Two simulated application examples demonstrate how interim design analyses can be integrated into common designs to inform sampling plans on the fly. The proposed methodology addresses the problem of sample size planning with sparse a-priori information and yields research designs that are efficient, informative, and flexible. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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使用贝叶斯因子预测进行中期设计分析。


实验设计的一个基本部分是确定研究的样本量。然而，数据收集之前有关总体参数和效应大小的信息稀疏，使得有效的样本量规划具有挑战性。具体来说，稀疏的信息可能导致研究设计基于不准确的先验假设，导致研究低效使用资源或产生不确定的结果。尽管它对样本量规划产生有害影响，但许多著名的实验设计方法未能充分解决稀疏先验信息的挑战。在这里，我们提出了一种用于临时设计分析的贝叶斯蒙特卡罗方法，使研究人员能够在整个研究过程中分析和调整他们的抽样计划。在任何时间点，该方法都会使用有关参数的最佳可用知识来预测预期的证据轨迹。两个模拟应用示例演示了如何将临时设计分析集成到通用设计中，以动态通知采样计划。所提出的方法解决了利用稀疏先验信息进行样本量规划的问题，并产生了高效、信息丰富且灵活的研究设计。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。