Bayesian Analysis of Generalized Linear Models with Historical
Data
Description
User-friendly functions for leveraging (multiple) historical data set(s) in Bayesian analysis of generalized
linear models (GLMs) and survival models, along with support for Bayesian model averaging (BMA). The package provides
functions for sampling from posterior distributions under various informative priors, including the prior induced by the
Bayesian hierarchical model, power prior by Ibrahim and Chen (2000) , normalized power prior by
Duan et al. (2006) , normalized asymptotic power prior by Ibrahim et al. (2015) ,
commensurate prior by Hobbs et al. (2011) , robust meta-analytic-predictive
prior by Schmidli et al. (2014) , latent exchangeability prior by Alt et al. (2024)
, and a normal (or half-normal) prior. The package also includes functions for computing
model averaging weights, such as BMA, pseudo-BMA, pseudo-BMA with the Bayesian bootstrap, and stacking (Yao et al.,
2018 ), as well as for generating posterior samples from the ensemble distributions to reflect
model uncertainty. In addition to GLMs, the package supports survival models including: (1) accelerated failure time
(AFT) models, (2) piecewise exponential (PWE) models, i.e., proportional hazards models with piecewise constant
baseline hazards, and (3) mixture cure rate models that assume a common probability of cure across subjects, paired
with a PWE model for the non-cured population. Functions for computing marginal log-likelihoods under each implemented
prior are also included. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package.