88 citations · 243 across the 16 of their papers we have counts for
17 papers
LEAP: The latent exchangeability prior for borrowing information from historical data
Ethan M. Alt, Xiuya Chang, Xun Jiang +4
It is becoming increasingly popular to elicit informative priors on the basis of historical data. Popular existing priors, including the power prior, commensurate prior, and robust…
Optimal Priors for the Discounting Parameter of the Normalized Power Prior
Yueqi Shen, Luiz M. Carvalho, Matthew A. Psioda +1
The power prior is a popular class of informative priors for incorporating information from historical data. It involves raising the likelihood for the historical data to a power,…
Deeply-Learned Generalized Linear Models with Missing Data
David K Lim, Naim U Rashid, Junier B Oliva +1
Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical…
BayesPPD: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Generalized Linear Models
Yueqi Shen, Matthew A. Psioda, Joseph G. Ibrahim
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…
A hierarchical prior for generalized linear models based on predictions for the mean response
Ethan M. Alt, Matthew A. Psioda, Joseph G. Ibrahim
There has been increased interest in using prior information in statistical analyses. For example, in rare diseases, it can be difficult to establish treatment efficacy based solel…
The scale transformed power prior for use with historical data from a different outcome model
Brady Nifong, Matthew A. Psioda, Joseph G. Ibrahim
We develop the scale transformed power prior for settings where historical and current data involve different data types, such as binary and continuous data, respectively. This sit…