Model diagnostics for the proportional hazards model with length-biased data

Chi Hyun Lee (), Jing Ning () and Yu Shen ()
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Chi Hyun Lee: The University of Texas MD Anderson Cancer Center
Jing Ning: The University of Texas MD Anderson Cancer Center
Yu Shen: The University of Texas MD Anderson Cancer Center

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 1, No 4, 79-96

Abstract: Abstract Length-biased data are frequently encountered in prevalent cohort studies. Many statistical methods have been developed to estimate the covariate effects on the survival outcomes arising from such data while properly adjusting for length-biased sampling. Among them, regression methods based on the proportional hazards model have been widely adopted. However, little work has focused on checking the proportional hazards model assumptions with length-biased data, which is essential to ensure the validity of inference. In this article, we propose a statistical tool for testing the assumed functional form of covariates and the proportional hazards assumption graphically and analytically under the setting of length-biased sampling, through a general class of multiparameter stochastic processes. The finite sample performance is examined through simulation studies, and the proposed methods are illustrated with the data from a cohort study of dementia in Canada.

Keywords: Dementia; Length-biased data; Model diagnostics; Proportional hazards model; Stochastic processes (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10985-018-9422-y

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