Accelerated failure time models with error-prone response and nonlinear covariates

As a specific application of survival analysis, one of main interests in medical studies aims to analyze the patients’ survival time of a specific cancer. Typically, gene expressions are treated as covariates to characterize the survival time. In the framework of survival analysis, the accelerated f...

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Vydáno v:Statistics and computing Ročník 34; číslo 6
Hlavní autor: Chen, Li-Pang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.12.2024
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Cancer
Computer Science
Error analysis
Error correction
Failure times
Nonlinear response
Original Paper
Probability and Statistics in Computer Science
Statistical Theory and Methods
Statistics and Computing/Statistics Programs
Survival
Survival analysis
Time measurement
Boosting
Regression calibration
Misclassification
Measurement error
Cubic spline
Variable selection
ISSN:0960-3174, 1573-1375
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Shrnutí:As a specific application of survival analysis, one of main interests in medical studies aims to analyze the patients’ survival time of a specific cancer. Typically, gene expressions are treated as covariates to characterize the survival time. In the framework of survival analysis, the accelerated failure time model in the parametric form is perhaps a common approach. However, gene expressions are possibly nonlinear and the survival time as well as censoring status are subject to measurement error. In this paper, we aim to tackle those complex features simultaneously. We first correct for measurement error in survival time and censoring status, and use them to develop a corrected Buckley–James estimator. After that, we use the boosting algorithm with the cubic spline estimation method to iteratively recover nonlinear relationship between covariates and survival time. Theoretically, we justify the validity of measurement error correction and estimation procedure. Numerical studies show that the proposed method improves the performance of estimation and is able to capture informative covariates. The methodology is primarily used to analyze the breast cancer data provided by the Netherlands Cancer Institute for research.
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ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-024-10491-9