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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| Vydané v: | Statistics and computing Ročník 34; číslo 6 |
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| Médium: | Journal Article |
| Jazyk: | English |
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01.12.2024
Springer Nature B.V |
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| ISSN: | 0960-3174, 1573-1375 |
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| Abstract | 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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| AbstractList | 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. |
| ArticleNumber | 183 |
| Author | Chen, Li-Pang |
| Author_xml | – sequence: 1 givenname: Li-Pang surname: Chen fullname: Chen, Li-Pang email: lchen723@nccu.edu.tw organization: Department of Statistics, National Chengchi University |
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| Keywords | Boosting Regression calibration Misclassification Measurement error Cubic spline Variable selection |
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| SubjectTerms | 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 |
| Title | Accelerated failure time models with error-prone response and nonlinear covariates |
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