arXiv subjects

Yueqi Shen

Publications and source records attributed to Yueqi Shen.

5 recordsLinked to original sources

arXiv2025

ANPP: the Adapted Normalized Power Prior for Borrowing Information from Multiple Historical Datasets in Clinical Trials

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, which acts as a discounting parameter. When the discounting parameter is modeled as random, the normalized power prior (NPP) is recommended. When there are multiple historical datasets, there has been limited research on how to choose priors for the multiple discounting parameters of the NPP to induce desirable information borrowing behavior. In this work, we address this question by investigating the analytical relationship between the NPP and the Bayesian hierarchical model (BHM), which is a widely used method for synthesizing information from different sources. We develop the adapted normalized power prior (ANPP), which establishes dependence between the dataset-specific discounting parameters of the NPP, leading to inferences that are identical to the BHM. We establish a direct relationship between the prior for the dataset-specific discounting parameters of the ANPP and the prior for the variance parameter of the BHM. Establishing this relationship not only justifies the NPP from the perspective of hierarchical modeling, but also achieves easy prior elicitation for the NPP for the purpose of dynamic borrowing. We examine the borrowing properties of the ANPP through simulations, and apply it to a case study for a pediatric lupus trial.

stat.ME↗

arXiv2024

Optimal Priors for the Discounting Parameter of the Normalized Power Prior

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, which acts as discounting parameter. When the discounting parameter is modelled as random, the normalized power prior is recommended. In this work, we prove that the marginal posterior for the discounting parameter for generalized linear models converges to a point mass at zero if there is any discrepancy between the historical and current data, and that it does not converge to a point mass at one when they are fully compatible. In addition, we explore the construction of optimal priors for the discounting parameter in a normalized power prior. In particular, we are interested in achieving the dual objectives of encouraging borrowing when the historical and current data are compatible and limiting borrowing when they are in conflict. We propose intuitive procedures for eliciting the shape parameters of a beta prior for the discounting parameter based on two minimization criteria, the Kullback-Leibler divergence and the mean squared error. Based on the proposed criteria, the optimal priors derived are often quite different from commonly used priors such as the uniform prior.

stat.ME↗

arXiv2024

BayesPPDSurv: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Time-To-Event Data

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 power priors incorporating historical data with for the analysis of time-to-event outcomes. The package implements the stratified proportional hazards regression model with piecewise constant hazard within each stratum. The package allows the historical data to inform the treatment effect parameter, parameter effects for other covariates in the regression model, as well as the baseline hazard parameters. The use of multiple historical datasets is supported. A novel algorithm is developed for computationally efficient use of the normalized power prior. In addition, the package supports the use of arbitrary sampling priors for computing Bayesian power and type I error rates, and has built-in features that semi-automatically generate sampling priors from the historical data. We demonstrate the use of BayesPPDSurv in a comprehensive case study for a melanoma clinical trial design.

stat.ME↗

arXiv2021

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 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.

stat.AP↗

arXiv2021

An Empirical Bayes Robust Meta-Analytical-Predictive Prior to Adaptively Leverage External Data

We propose a novel empirical Bayes robust MAP (EB-rMAP) prior to adaptively leverage external/historical data. Built on Box's prior predictive p-value, the EB-rMAP prior framework balances between model parsimony and flexibility through a tuning parameter. The proposed framework can be applied to binary, normal, and time-to-event endpoints. Computational aspects of the framework are efficient. Simulations results with different endpoints demonstrate that the EB-rMAP prior is robust in the presence of prior-data conflict while preserving statistical power. The proposed EB-rMAP prior is then applied to a clinical dataset that comprises of ten oncology clinical trials, including the perspective study.

stat.ME↗