ArticleBiometrics2022
Cox regression model under dependent truncation.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Exploring the association between common genetic deletions and aging: insights from the Canadian Longitudinal Study on Aging.GeroScience · 2026Article
- A New Inverse Probability of Selection Weighted Cox Model to Deal With Outcome-Dependent Sampling in Survival Analysis.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Robust inverse probability weighted estimators for doubly truncated Cox regression with closed-form standard errors.Lifetime data analysis · 2025Article
- Regression Modeling of Cumulative Incidence Function for Left-Truncated Right-Censored Competing Risks Data: A Modified Pseudo-observation Approach.Communications in statistics: theory and methods · 2025Article
- Goodness-of-Fit Testing for a Regression Model With a Doubly Truncated Response.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Reliability analysis based on doubly-truncated and interval-censored data.Journal of applied statistics · 2025Article
- Cox regression model under dependent truncation.Biometrics · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
Truncation is a statistical phenomenon that occurs in many time-to-event studies. For example, autopsy-confirmed studies of neurodegenerative diseases are subject to an inherent left and right truncation, also known as double truncation. When the goal is to study the effect of risk factors on survival, the standard Cox regression model cannot be used when the survival time is subject to truncation. Existing methods that adjust for both left and right truncation in the Cox regression model require independence between the survival times and truncation times, which may not be a reasonable assumption in practice. We propose an expectation-maximization algorithm to relax the independence assumption in the Cox regression model under left, right, or double truncation to an assumption of conditional independence on the observed covariates. The resulting regression coefficient estimators are consistent and asymptotically normal. We demonstrate through extensive simulations that the proposed estimator has little bias and has a similar or lower mean-squared error compared to existing estimators. We implement our approach to assess the effect of occupation on survival in subjects with autopsy-confirmed Alzheimer's disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.