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December 2019 Joint model of accelerated failure time and mechanistic nonlinear model for censored covariates, with application in HIV/AIDS
Hongbin Zhang, Lang Wu
Ann. Appl. Stat. 13(4): 2140-2157 (December 2019). DOI: 10.1214/19-AOAS1274

Abstract

For a time-to-event outcome with censored time-varying covariates, a joint Cox model with a linear mixed effects model is the standard modeling approach. In some applications such as AIDS studies, mechanistic nonlinear models are available for some covariate process such as viral load during anti-HIV treatments, derived from the underlying data-generation mechanisms and disease progression. Such a mechanistic nonlinear covariate model may provide better-predicted values when the covariates are left censored or mismeasured. When the focus is on the impact of the time-varying covariate process on the survival outcome, an accelerated failure time (AFT) model provides an excellent alternative to the Cox proportional hazard model since an AFT model is formulated to allow the influence of the outcome by the entire covariate process. In this article, we consider a nonlinear mixed effects model for the censored covariates in an AFT model, implemented using a Monte Carlo EM algorithm, under the framework of a joint model for simultaneous inference. We apply the joint model to an HIV/AIDS data to gain insights for assessing the association between viral load and immunological restoration during antiretroviral therapy. Simulation is conducted to compare model performance when the covariate model and the survival model are misspecified.

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Hongbin Zhang. Lang Wu. "Joint model of accelerated failure time and mechanistic nonlinear model for censored covariates, with application in HIV/AIDS." Ann. Appl. Stat. 13 (4) 2140 - 2157, December 2019. https://doi.org/10.1214/19-AOAS1274

Information

Received: 1 May 2018; Revised: 1 April 2019; Published: December 2019
First available in Project Euclid: 28 November 2019

zbMATH: 07160934
MathSciNet: MR4037425
Digital Object Identifier: 10.1214/19-AOAS1274

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 4 • December 2019
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