BayesPPD: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Generalized Linear Models

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 subj...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-12
Main Authors: Shen, Yueqi, Psioda, Matthew A, Ibrahim, Joseph G
Format: Article
Language:English
Subjects:
Approximation
Asymptotic methods
Bayesian analysis
Generalized linear models
Sampling
Size determination
Statistical models
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 \(a_0\) 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.
ISSN:2331-8422