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NP-LEAP: Nonparametric Latent Exchangeability Prior for Model-Lean Borrowing from Historical Data
Ethan M. Alt, Miheer Dewaskar, Jacob M. Maronge +2
Bayesian dynamic borrowing (BDB) methods leverage historical data to reduce treatment effect uncertainty, yet existing approaches rely on parametric outcome models susceptible to m…
BayesPPDSurv: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Time-To-Event Data
Yueqi Shen, Matthew A. Psioda, Joseph G. Ibrahim
The BayesPPDSurv (Bayesian Power Prior Design for Survival Data) R package supports Bayesian power and type I error calculations and model fitting using the power and normalized po…
ANPP: the Adapted Normalized Power Prior for Borrowing Information from Multiple Historical Datasets in Clinical Trials
Yueqi Shen, Matthew A. Psioda, Luiz M. Carvalho +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,…
Case Weighted Adaptive Power Priors for Hybrid Control Analyses with Time-to-Event Data
Evan Kwiatkowski, Jiawen Zhu, Xiao Li +3
We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCT) where the degree of borrowing is determin…
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…