Long-Term Effect Estimation When Combining Clinical Trial and Observational Follow-Up Datasets

• 成果类型: Article; Early Access
• 署名作者: Cheng, Gang; Chen, Yen-Chi; Unger, Joseph M.; Till, Cathee; Zhao, Ying-Qi
• 署名单位: University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2526703
• 发表日期: 2025
• 关键词: proportional hazards models; cox regression-model; change-point; likelihood-estimation; weighted likelihood; inference

摘要

Combining experimental and observational follow-up datasets has received much attention lately. In a survival setting, recent work has used Medicare claims to extend the follow-up period for participants in a prostate cancer clinical trial. This allows the estimation of the long-term effect that cannot be estimated by the trial data alone. In this article, we study the estimation of long-term effect when participants in a clinical trial are linked to an observational follow-up dataset. Such linkages are often incomplete and we formulate incomplete linkages as a missing data problem. We use the popular Cox model to define the long-term effect and we propose two approaches to deal with the missing data problem. The first approach, termed non-linked-as-censored (NLAC), is a simple approach that works when Cox model is correctly specified and linkage satisfies a conditionally independent assumption. To gain robustness against model mis-specification, we propose an inverse probability of linkage weighted approach, along with the augmented inverse probability of weighted method, based on a novel conditional linking at random (CLAR) assumption. We further extend our approach to incorporate time-dependent covariates. Simulation results confirm the validity of our method and we apply our methods to the SWOG study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.