Quantile regression for survival data with covariates subject to detection limits
With advances in biomedical research, biomarkers are becoming increasingly important prognostic factors for predicting overall survival, while the measurement of biomarkers is often censored due to instruments' lower limits of detection. This leads to two types of censoring: random censoring in...
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sg-ntu-dr.10356-1546352021-12-30T03:02:41Z Quantile regression for survival data with covariates subject to detection limits Yu, Tonghui Xiang, Liming Wang, Huixia Judy School of Physical and Mathematical Sciences Science::Mathematics Censoring Detection Limit With advances in biomedical research, biomarkers are becoming increasingly important prognostic factors for predicting overall survival, while the measurement of biomarkers is often censored due to instruments' lower limits of detection. This leads to two types of censoring: random censoring in overall survival outcomes and fixed censoring in biomarker covariates, posing new challenges in statistical modeling and inference. Existing methods for analyzing such data focus primarily on linear regression ignoring censored responses or semiparametric accelerated failure time models with covariates under detection limits (DL). In this paper, we propose a quantile regression for survival data with covariates subject to DL. Comparing to existing methods, the proposed approach provides a more versatile tool for modeling the distribution of survival outcomes by allowing covariate effects to vary across conditional quantiles of the survival time and requiring no parametric distribution assumptions for outcome data. To estimate the quantile process of regression coefficients, we develop a novel multiple imputation approach based on another quantile regression for covariates under DL, avoiding stringent parametric restrictions on censored covariates as often assumed in the literature. Under regularity conditions, we show that the estimation procedure yields uniformly consistent and asymptotically normal estimators. Simulation results demonstrate the satisfactory finite-sample performance of the method. We also apply our method to the motivating data from a study of genetic and inflammatory markers of Sepsis. Ministry of Education (MOE) NationalScienceFoundation,Grant/AwardNumber:DMS-1712760;SingaporeMinistryofEducationAcademicResearchFund,Grant/AwardNumber:Tier1grantRG134/17(S) 2021-12-30T03:02:41Z 2021-12-30T03:02:41Z 2021 Journal Article Yu, T., Xiang, L. & Wang, H. J. (2021). Quantile regression for survival data with covariates subject to detection limits. Biometrics, 77(2), 610-621. https://dx.doi.org/10.1111/biom.13309 0006-341X https://hdl.handle.net/10356/154635 10.1111/biom.13309 32453884 2-s2.0-85086123452 2 77 610 621 en RG134/17(S) Biometrics © 2020 The International Biometric Society. All rights reserved. |
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Science::Mathematics Censoring Detection Limit Yu, Tonghui Xiang, Liming Wang, Huixia Judy Quantile regression for survival data with covariates subject to detection limits |
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With advances in biomedical research, biomarkers are becoming increasingly important prognostic factors for predicting overall survival, while the measurement of biomarkers is often censored due to instruments' lower limits of detection. This leads to two types of censoring: random censoring in overall survival outcomes and fixed censoring in biomarker covariates, posing new challenges in statistical modeling and inference. Existing methods for analyzing such data focus primarily on linear regression ignoring censored responses or semiparametric accelerated failure time models with covariates under detection limits (DL). In this paper, we propose a quantile regression for survival data with covariates subject to DL. Comparing to existing methods, the proposed approach provides a more versatile tool for modeling the distribution of survival outcomes by allowing covariate effects to vary across conditional quantiles of the survival time and requiring no parametric distribution assumptions for outcome data. To estimate the quantile process of regression coefficients, we develop a novel multiple imputation approach based on another quantile regression for covariates under DL, avoiding stringent parametric restrictions on censored covariates as often assumed in the literature. Under regularity conditions, we show that the estimation procedure yields uniformly consistent and asymptotically normal estimators. Simulation results demonstrate the satisfactory finite-sample performance of the method. We also apply our method to the motivating data from a study of genetic and inflammatory markers of Sepsis. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Yu, Tonghui Xiang, Liming Wang, Huixia Judy |
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Article |
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Yu, Tonghui Xiang, Liming Wang, Huixia Judy |
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Yu, Tonghui |
title |
Quantile regression for survival data with covariates subject to detection limits |
title_short |
Quantile regression for survival data with covariates subject to detection limits |
title_full |
Quantile regression for survival data with covariates subject to detection limits |
title_fullStr |
Quantile regression for survival data with covariates subject to detection limits |
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Quantile regression for survival data with covariates subject to detection limits |
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quantile regression for survival data with covariates subject to detection limits |
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2021 |
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https://hdl.handle.net/10356/154635 |
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