Joint modeling of longitudinal and survival data with the Cox model and two-phase sampling

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• 作者：Rong Fu ; Peter B. Gilbert
• 关键词：Case ; cohort ; Measurement error ; Proportional hazards model ; Prentice surrogate endpoint evaluation ; Random effects model
• 刊名：Lifetime Data Analysis
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：23
• 期：1
• 页码：136-159
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics, general; Statistics for Life Sciences, Medicine, Health Sciences; Quality Control, Reliability, Safety and Risk; Statistics for Business/Economics/Mathematical Finance/Insurance; Operation
• 出版者：Springer US
• ISSN：1572-9249
• 卷排序：23

文摘

A common objective of cohort studies and clinical trials is to assess time-varying longitudinal continuous biomarkers as correlates of the instantaneous hazard of a study endpoint. We consider the setting where the biomarkers are measured in a designed sub-sample (i.e., case-cohort or two-phase sampling design), as is normative for prevention trials. We address this problem via joint models, with underlying biomarker trajectories characterized by a random effects model and their relationship with instantaneous risk characterized by a Cox model. For estimation and inference we extend the conditional score method of Tsiatis and Davidian (Biometrika 88(2):447–458, 2001) to accommodate the two-phase biomarker sampling design using augmented inverse probability weighting with nonparametric kernel regression. We present theoretical properties of the proposed estimators and finite-sample properties derived through simulations, and illustrate the methods with application to the AIDS Clinical Trials Group 175 antiretroviral therapy trial. We discuss how the methods are useful for evaluating a Prentice surrogate endpoint, mediation, and for generating hypotheses about biological mechanisms of treatment efficacy.