Quantile regression for survival data with covariates subject to detection limits

• Published in: Biometrics Vol. 77; no. 2; pp. 610 - 621
• Main Authors: Yu, Tonghui, Xiang, Liming, Wang, Huixia Judy
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.06.2021
• Subjects: Biomarkers; biomedical research; censoring; detection limit; Failure times; Inflammation; Mathematical models; Medical prognosis; Medical research; multiple imputation; quantile regression; Quantiles; regression analysis; Regression coefficients; Sepsis; shrinkage; Statistical analysis; Statistical inference; Statistical models; Survival; survival data; quantile regression; survival data; multiple imputation; censoring; detection limit; shrinkage

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...