Variable selection and estimation for the additive hazards model subject to left-truncation, right-censoring and measurement error in covariates

Variable selection with censored survival data is of great practical importance, and several methods have been proposed for variable selection based on different models. However, the impacts of biased samples caused by left-truncation and covariate measurement error to variable selection are not ful...

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Bibliographic Details
Published inJournal of statistical computation and simulation Vol. 90; no. 18; pp. 3261 - 3300
Main Author Chen, Li-Pang
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 11.12.2020
Taylor & Francis Ltd
Subjects
Biased sample
Error analysis
Error correction
Hazards
mismeasurement
Parameter estimation
prevalent cohort
pseudo likelihood
SIMEX
Survival
variable selection
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Summary:Variable selection with censored survival data is of great practical importance, and several methods have been proposed for variable selection based on different models. However, the impacts of biased samples caused by left-truncation and covariate measurement error to variable selection are not fully explored. In this paper, we mainly focus on the additive hazards model and analyze variable selection and estimation for survival data subject to left-truncation and measurement error in covariates. We develop the three-stage procedure to correct for error effects, select informative variables, and estimate the parameters of interest simultaneously. Numerical studies are reported to assess the performance of the proposed methods.
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ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2020.1800705