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Interim Design Analysis Using Bayes Factor Forecasts
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DOI：10.1037/met0000641.png)
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Abstract
En 中文
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. The scientific relevance of rigorous study designs has often been compared to the importance of architectural plans. Without a proper plan, a building may collapse. Without rigorous research design, a study may not withstand scientific scrutiny. However, unlike architects, researchers rarely reevaluate their designs after the start of a study. In this article, we outline a framework for intermediate design analyses that capitalizes on the advantages of the Bayesian statistical approach, and allows researchers to readjust their sample sizes based on information gathered over the course of a study. Using two application examples, we discuss how interim design analyses can increase the informativeness and efficiency of study designs in practice.
Keywords:
Bayesian inference
sample size determination
Bayes factor design analysis
experimental design
sequential testing
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