1 citations · 3 across the 10 of their papers we have counts for
10 papers
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…
Statistical Design and Rationale of the Biomarkers for Evaluating Spine Treatments (BEST) Trial
John Sperger, Kelley M. Kidwell, Matthew C. Mauck +16
Chronic low back pain (cLBP) is a prevalent condition with profound impacts on functioning and quality of life. While multiple evidence-based treatments exist, they all have modest…
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…
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,…
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…
Bayesian Multivariate Probability of Success Using Historical Data with Strict Control of Family-wise Error Rate
Ethan M. Alt, Matthew A. Psioda, Joseph G. Ibrahim
Given the cost and duration of phase III and phase IV clinical trials, the development of statistical methods for go/no-go decisions is vital. In this paper, we introduce a Bayesia…