2021/12/29 by Yueqi Shen, Shen, Yueqi, Matthew A. Psioda +3 · 2 citations
Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2112.14616
openalex publication_date 2021/12/29 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
The R package BayesPPD (Bayesian Power Prior Design) supports Bayesian power and type I error calculation and model fitting after incorporating historical data with the power prior and the normalized power prior for generalized linear models (GLM). The package accommodates summary level data or subject level data with covariate information. It supports use of multiple historical datasets as well as design without historical data. Supported distributions for responses include normal, binary (Bernoulli/binomial), Poisson and exponential. The power parameter a0 can be fixed or modeled as random using a normalized power prior for each of these distributions. In addition, the package supports the use of arbitrary sampling priors for computing Bayesian power and type I error rates, and has specific features for GLMs that semi-automatically generate sampling priors from historical data. Since sample size determination (SSD) for GLMs is computationally intensive, an approximation method based on asymptotic theory has been implemented to support applications using the power prior. In addition to describing the statistical methodology and functions implemented in the package to enable SSD, we also demonstrate the use of BayesPPD in two comprehensive case studies.