ArticleBiometrical journal. Biometrische Zeitschrift2019
Cox regression model with randomly censored covariates.
Article in Biometrical journal. Biometrische Zeitschrift, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- SPARCC: Semi-Parametric Robust Estimation in a Right-Censored Covariate Model.Journal of the American Statistical Association · 2026Article
- Subgroups of Clinical High Risk for Psychosis Based on Baseline Antipsychotic Exposure: Clinical and Outcome Comparisons Across a 2-Year Follow-up Period.Schizophrenia bulletin · 2025Article
- Baseline Exposure to Antipsychotic Medication in Young People at Clinical High Risk for Psychosis: A 2-Year Italian Follow-Up Study.Human psychopharmacology · 2025Article
- Extrapolation before imputation reduces bias when imputing censored covariates.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- New insights into multicollinearity in the Cox proportional hazard models: the Kibria-Lukman estimator and its application.Journal of applied statistics · 2025Article
- Rates and predictors of service disengagement in adolescents with first episode psychosis: results from the 2-year follow-up of the Pr-EP program.European child & adolescent psychiatry · 2024Article
- Short-term disengagement from early intervention service for first-episode psychosis: findings from the "Parma Early Psychosis" program.Social psychiatry and psychiatric epidemiology · 2024Article
- Adverse outcome analysis in people at clinical high risk for psychosis: results from a 2-year Italian follow-up study.Social psychiatry and psychiatric epidemiology · 2024Article
- Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease.Annual review of statistics and its application · 2024Article
- Understanding the implications of a complete case analysis for regression models with a right-censored covariate.The American statistician · 2024Article
- Differential Expression of Autophagy-Related Long Non-Coding RNA in Melanoma.Bulletin of experimental biology and medicine · 2023Article
- Semiparametric analysis of a generalized linear model with multiple covariates subject to detection limits.Statistics in medicine · 2022Article
- Correcting conditional mean imputation for censored covariates and improving usability.Biometrical journal. Biometrische Zeitschrift · 2022Article
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Authors and funding
3 authors.
Funding
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.
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