Regression analysis of additive hazards model with sparse longitudinal covariates

Zhuowei Sun, Hongyuan Cao and Li Chen
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Zhuowei Sun: Jilin University
Hongyuan Cao: Jilin University
Li Chen: East Hanover

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 2, No 5, 263-281

Abstract: Abstract Additive hazards model is often used to complement the proportional hazards model in the analysis of failure time data. Statistical inference of additive hazards model with time-dependent longitudinal covariates requires the availability of the whole trajectory of the longitudinal process, which is not realistic in practice. The commonly used last value carried forward approach for intermittently observed longitudinal covariates can induce biased parameter estimation. The more principled joint modeling of the longitudinal process and failure time data imposes strong modeling assumptions, which is difficult to verify. In this paper, we propose methods that weigh the distance between the observational time of longitudinal covariates and the failure time, resulting in unbiased regression coefficient estimation. We establish the consistency and asymptotic normality of the proposed estimators. Simulation studies provide numerical support for the theoretical findings. Data from an Alzheimer’s study illustrate the practical utility of the methodology.

Keywords: Additive hazards modela; Last value imputation; Time-varying covariates (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09548-6

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