Quantile residual life regression with longitudinal biomarker measurements for dynamic prediction

Ruosha Li, Xuelin Huang, Jorge Cortes

Research output: Contribution to journalArticle

Abstract

Residual life is of great interest to patients with life threatening disease. It is also important for clinicians who estimate prognosis and make treatment decisions. Quantile residual life has emerged as a useful summary measure of the residual life. It has many desirable features, such as robustness and easy interpretation. In many situations, the longitudinally collected biomarkers during patients' follow-up visits carry important prognostic value. In this work, we study quantile regression methods that allow for dynamic predictions of the quantile residual life, by flexibly accommodating the post-baseline biomarker measurements in addition to the baseline covariates. We propose unbiased estimating equations that can be solved via existing L1-minimization algorithms. The resulting estimators have desirable asymptotic properties and satisfactory finite sample performance. We apply our method to a study of chronic myeloid leukaemia to demonstrate its usefulness as a dynamic prediction tool.

Original languageEnglish (US)
Pages (from-to)755-773
Number of pages19
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume65
Issue number5
DOIs
StatePublished - Nov 1 2016
Externally publishedYes

Fingerprint

Residual Life
Biomarkers
Quantile
Regression
Prediction
Baseline
Quantile Regression
Estimating Equation
Prognosis
Leukemia
Asymptotic Properties
Covariates
Robustness
Estimator
Estimate
Demonstrate

Keywords

  • Chronic myeloid leukaemia
  • Dynamic prediction
  • Quantile regression
  • Residual life
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Quantile residual life regression with longitudinal biomarker measurements for dynamic prediction. / Li, Ruosha; Huang, Xuelin; Cortes, Jorge.

In: Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol. 65, No. 5, 01.11.2016, p. 755-773.

Research output: Contribution to journalArticle

@article{2c8f886eba304e779b63277f602c2cc2,
title = "Quantile residual life regression with longitudinal biomarker measurements for dynamic prediction",
abstract = "Residual life is of great interest to patients with life threatening disease. It is also important for clinicians who estimate prognosis and make treatment decisions. Quantile residual life has emerged as a useful summary measure of the residual life. It has many desirable features, such as robustness and easy interpretation. In many situations, the longitudinally collected biomarkers during patients' follow-up visits carry important prognostic value. In this work, we study quantile regression methods that allow for dynamic predictions of the quantile residual life, by flexibly accommodating the post-baseline biomarker measurements in addition to the baseline covariates. We propose unbiased estimating equations that can be solved via existing L1-minimization algorithms. The resulting estimators have desirable asymptotic properties and satisfactory finite sample performance. We apply our method to a study of chronic myeloid leukaemia to demonstrate its usefulness as a dynamic prediction tool.",
keywords = "Chronic myeloid leukaemia, Dynamic prediction, Quantile regression, Residual life, Survival analysis",
author = "Ruosha Li and Xuelin Huang and Jorge Cortes",
year = "2016",
month = "11",
day = "1",
doi = "10.1111/rssc.12152",
language = "English (US)",
volume = "65",
pages = "755--773",
journal = "Journal of the Royal Statistical Society. Series C: Applied Statistics",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "5",

}

TY - JOUR

T1 - Quantile residual life regression with longitudinal biomarker measurements for dynamic prediction

AU - Li, Ruosha

AU - Huang, Xuelin

AU - Cortes, Jorge

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Residual life is of great interest to patients with life threatening disease. It is also important for clinicians who estimate prognosis and make treatment decisions. Quantile residual life has emerged as a useful summary measure of the residual life. It has many desirable features, such as robustness and easy interpretation. In many situations, the longitudinally collected biomarkers during patients' follow-up visits carry important prognostic value. In this work, we study quantile regression methods that allow for dynamic predictions of the quantile residual life, by flexibly accommodating the post-baseline biomarker measurements in addition to the baseline covariates. We propose unbiased estimating equations that can be solved via existing L1-minimization algorithms. The resulting estimators have desirable asymptotic properties and satisfactory finite sample performance. We apply our method to a study of chronic myeloid leukaemia to demonstrate its usefulness as a dynamic prediction tool.

AB - Residual life is of great interest to patients with life threatening disease. It is also important for clinicians who estimate prognosis and make treatment decisions. Quantile residual life has emerged as a useful summary measure of the residual life. It has many desirable features, such as robustness and easy interpretation. In many situations, the longitudinally collected biomarkers during patients' follow-up visits carry important prognostic value. In this work, we study quantile regression methods that allow for dynamic predictions of the quantile residual life, by flexibly accommodating the post-baseline biomarker measurements in addition to the baseline covariates. We propose unbiased estimating equations that can be solved via existing L1-minimization algorithms. The resulting estimators have desirable asymptotic properties and satisfactory finite sample performance. We apply our method to a study of chronic myeloid leukaemia to demonstrate its usefulness as a dynamic prediction tool.

KW - Chronic myeloid leukaemia

KW - Dynamic prediction

KW - Quantile regression

KW - Residual life

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=84963812205&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84963812205&partnerID=8YFLogxK

U2 - 10.1111/rssc.12152

DO - 10.1111/rssc.12152

M3 - Article

AN - SCOPUS:84963812205

VL - 65

SP - 755

EP - 773

JO - Journal of the Royal Statistical Society. Series C: Applied Statistics

JF - Journal of the Royal Statistical Society. Series C: Applied Statistics

SN - 0035-9254

IS - 5

ER -