A Commensurate Prior Model With Random Effects for Survival and Competing Risk Outcomes to Accommodate Historical Controls. Pharm Stat 2025;24(1):e2464
Date
01/23/2025Pubmed ID
39846144Pubmed Central ID
PMC12147052DOI
10.1002/pst.2464Scopus ID
2-s2.0-85216016393 (requires institutional sign-in at Scopus site) 1 CitationAbstract
Clinical trials (CTs) often suffer from small sample sizes due to limited budgets and patient enrollment challenges. Using historical data for the CT data analysis may boost statistical power and reduce the required sample size. Existing methods on borrowing information from historical data with right-censored outcomes did not consider matching between historical data and CT data to reduce the heterogeneity. In addition, they studied the survival outcome only, not competing risk outcomes. Therefore, we propose a clustering-based commensurate prior model with random effects for both survival and competing risk outcomes that effectively borrows information based on the degree of comparability between historical and CT data. Simulation results show that the proposed method controls type I errors better and has a lower bias than some competing methods. We apply our method to a phase III CT which compares the effectiveness of bone marrow donated from family members with only partially matched bone marrow versus two partially matched cord blood units to treat leukemia and lymphoma.
Author List
Khanal M, Logan BR, Banerjee A, Fang X, Ahn KWAuthors
Kwang Woo Ahn PhD Director, Professor in the Data Science Institute department at Medical College of WisconsinAnjishnu Banerjee PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
Xi Fang Assistant Professor in the Data Science Institute department at Medical College of Wisconsin
Brent R. Logan PhD Director, Professor in the Data Science Institute department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Bone Marrow TransplantationClinical Trials, Phase III as Topic
Computer Simulation
Humans
Leukemia
Lymphoma
Models, Statistical
Sample Size
Survival Analysis
