Cox regression model with randomly censored covariates

This paper deals with a Cox proportional hazards regression model, where some covariates of interest are randomly right‐censored. While methods for censored outcomes have become ubiquitous in the literature, methods for censored covariates have thus far received little attention and, for the most pa...

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Bibliographic Details
Published inBiometrical journal Vol. 61; no. 4; pp. 1020 - 1032
Main Authors Atem, Folefac D., Matsouaka, Roland A., Zimmern, Vincent E.
Format Journal Article
LanguageEnglish
Published Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.07.2019
Subjects
Adolescent
Adult
Age of Onset
Aged
Biometry - methods
Cardiovascular Diseases - epidemiology
censored covariate
Child
complete‐case analysis
Computer simulation
Cox proportional hazards model
Data analysis
Economic models
Female
Hazards
Heart
Humans
Male
Methods
Middle Aged
Multivariate Analysis
Offspring
Proportional Hazards Models
random censoring
Regression Analysis
Regression models
Statistical analysis
Statistical models
Statistics, Nonparametric
survival analysis
Young Adult
complete-case analysis
random censoring
survival analysis
censored covariate
Cox proportional hazards model
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Summary:This paper deals with a Cox proportional hazards regression model, where some covariates of interest are randomly right‐censored. While methods for censored outcomes have become ubiquitous in the literature, methods for censored covariates have thus far received little attention and, for the most part, dealt with the issue of limit‐of‐detection. For randomly censored covariates, an often‐used method is the inefficient complete‐case analysis (CCA) which consists in deleting censored observations in the data analysis. When censoring is not completely independent, the CCA leads to biased and spurious results. Methods for missing covariate data, including type I and type II covariate censoring as well as limit‐of‐detection do not readily apply due to the fundamentally different nature of randomly censored covariates. We develop a novel method for censored covariates using a conditional mean imputation based on either Kaplan–Meier estimates or a Cox proportional hazards model to estimate the effects of these covariates on a time‐to‐event outcome. We evaluate the performance of the proposed method through simulation studies and show that it provides good bias reduction and statistical efficiency. Finally, we illustrate the method using data from the Framingham Heart Study to assess the relationship between offspring and parental age of onset of cardiovascular events.
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ISSN:0323-3847
1521-4036
DOI:10.1002/bimj.201800275