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Lifetime Data Anal. 2017 Jan;23(1):136-159. doi: 10.1007/s10985-016-9364-1. Epub 2016 Mar 23.

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

Author information

1
Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA. rongf@uw.edu.
2
Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA.
3
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.

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; Prentice surrogate endpoint evaluation; Proportional hazards model; Random effects model

PMID:
27007859
PMCID:
PMC5035179
DOI:
10.1007/s10985-016-9364-1
[Indexed for MEDLINE]
Free PMC Article

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