The Cox Model With Adaptive Fused Group Bridge Penalty to Incorporate Historical Data Into the Analysis of Clinical Trials With an Application to BMT CTN 1101
The incorporation of historical data (HD) into a clinical trial analysis can improve the precision and efficiency of treatment evaluation if the HD are exchangeable with clinical trial data. Evaluating the exchangeability of these two data sets is challenging, however, as an incorrect assessment of exchangeability yields invalid inference on the treatment effect that may produce bias and inflate the Type I error rate. To address this practical problem, we propose an adaptive fused group bridge penalty to evaluate the comparability of parameters between HD and clinical trial data and make inferences on the treatment effect. The proposed penalty has oracle properties, including consistency for identifying the underlying model and the asymptotic normality of the estimators. Simulation studies show that the proposed method controls the Type I error rate better and has higher power than competing methods under both exchangeable and non-exchangeable settings. We apply the proposed method by reanalyzing a Phase III trial while also leveraging a corresponding HD set.
Authors
- Michael J. Martens (ORCID: https://orcid.org/0000-0003-3799-681X)
- Kwang Woo Ahn (ORCID: https://orcid.org/0000-0003-4567-8037)
- Brent R. Logan (ORCID: https://orcid.org/0000-0002-4473-4044)
- Soyoung Kim (ORCID: https://orcid.org/0000-0003-1404-0575)
- Xi Fang (ORCID: https://orcid.org/0000-0002-6692-5730)
Institutions
- Medical College of Wisconsin (US)
Publication Details
- Journal
- Statistics in Medicine
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/sim.70233
- Primary Topic
- Statistical Methods in Clinical Trials
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Health and Human Services
- National Institutes of Health
- Health Resources and Services Administration
- National Heart, Lung, and Blood Institute
- Office of Naval Research
- National Cancer Institute
- National Institute of Allergy and Infectious Diseases
- National Center for Advancing Translational Sciences