Cox regression model with randomly censored covariates.

Pubmed ID: 30908720

Pubmed Central ID: PMC6922001

Journal: Biometrical journal. Biometrische Zeitschrift

Publication Date: July 1, 2019

Affiliation: Department of Pediatrics, Children Hospital Dallas, Dallas, TX, USA.

Link: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.201800275

MeSH Terms: Humans, Male, Adult, Female, Aged, Cardiovascular Diseases, Adolescent, Middle Aged, Proportional Hazards Models, Regression Analysis, Multivariate Analysis, Young Adult, Child, Statistics, Nonparametric, Age of Onset, Biometry

Grants: L30 TR002132, UL1 TR001117

Authors: Atem FD, Matsouaka RA, Zimmern VE

Cite As: Atem FD, Matsouaka RA, Zimmern VE. Cox regression model with randomly censored covariates. Biom J 2019 Jul;61(4):1020-1032. Epub 2019 Mar 25.

Studies:

Abstract

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.