Abstract
Combining historical control data with current control data may reduce the necessary study size of a clinical trial. However, this only applies when the historical control data are similar enough to the current control data. Several Bayesian approaches for incorporating historical data in a dynamic way have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP). Here we discuss the generalization of the MPP approach for multiple historical control groups for the linear regression model. This approach is useful when the controls differ more than in a random way, but become again (approximately) exchangeable conditional on covariates. The proposed approach builds on the approach previously developed for binary outcomes by some of the current authors. Two MPP approaches have been developed with multiple controls. The first approach assumes independent powers, while in the second approach the powers have a hierarchical structure. We conducted several simulation studies to investigate the frequentist characteristics of borrowing methods and analyze a real-life data set. When there is between-study variation in the slopes of the model or in the covariate distributions, the MPP approach achieves approximately nominal type I error rates and greater power than the MAP prior, provided that the covariates are included in the model. When the intercepts vary, the MPP yields a slightly inflated type I error rate, whereas the MAP does not. We conclude that our approach is a worthy competitor to the MAP approach for the linear regression case.
| Original language | English |
|---|---|
| Pages (from-to) | 418-438 |
| Number of pages | 21 |
| Journal | Pharmaceutical Statistics |
| Volume | 21 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Mar 2022 |
Bibliographical note
Funding Information:Data collection and sharing for this project was funded by the University of California, San Diego Alzheimer's Disease Cooperative Study (ADCS) (National Institute on Aging Grant Number U01AG010483). For the simulations we used the infrastructure of the VSC - Flemish Supercomputer Center, funded by the Hercules foundation and the Flemish Government - department EWI. The authors acknowledge the BOF bilateral cooperation of UHasselt for the financial support to the first author for his research visits. We would like to thank Professor Ziv Shkedy for his valuable comments on the manuscript. The unreserved support of Mr. Hongchao Qi during data management is duly acknowledged.
Funding Information:
Data collection and sharing for this project was funded by the University of California, San Diego Alzheimer's Disease Cooperative Study (ADCS) (National Institute on Aging Grant Number U01AG010483). For the simulations we used the infrastructure of the VSC ‐ Flemish Supercomputer Center, funded by the Hercules foundation and the Flemish Government ‐ department EWI. The authors acknowledge the BOF bilateral cooperation of UHasselt for the financial support to the first author for his research visits. We would like to thank Professor Ziv Shkedy for his valuable comments on the manuscript. The unreserved support of Mr. Hongchao Qi during data management is duly acknowledged.
Publisher Copyright:
© 2021 John Wiley & Sons Ltd.