Analysis of length‐biased and partly interval‐censored survival data with mismeasured covariates

In this paper, we analyze the length‐biased and partly interval‐censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing methods that focus on low‐dimensional data and assume the covariates to be precisely measured, sometimes re...

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Bibliographic Details
Published in:Biometrics 2023-12, Vol.79 (4), p.3929-3940
Main Authors: Chen, Li‐Pang, Qiu, Bangxu
Format: Article
Language:English
Subjects:
AFT model
biased sampling
Biostatistics
boosting
Buckley–James formulation
Computer Simulation
Error analysis
Error correction
Failure times
Feature selection
incomplete data
measurement error correction
Sample Size
SIMEX
Survival
Survival Analysis
variable selection
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Summary:In this paper, we analyze the length‐biased and partly interval‐censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing methods that focus on low‐dimensional data and assume the covariates to be precisely measured, sometimes researchers may encounter high‐dimensional data subject to measurement error, which are ubiquitous in applications and make estimation unreliable. To address those challenges, we explore a valid inference method for handling high‐dimensional length‐biased and interval‐censored survival data with measurement error in covariates under the accelerated failure time model. We primarily employ the SIMEX method to correct for measurement error effects and propose the boosting procedure to do variable selection and estimation. The proposed method is able to handle the case that the dimension of covariates is larger than the sample size and enjoys appealing features that the distributions of the covariates are left unspecified.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.13898