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

Rong Fu () and Peter B. Gilbert ()
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Rong Fu: University of Washington
Peter B. Gilbert: University of Washington

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2017, vol. 23, issue 1, No 8, 136-159

Abstract: Abstract 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.

Keywords: Case-cohort; Measurement error; Proportional hazards model; Prentice surrogate endpoint evaluation; Random effects model (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10985-016-9364-1

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