Quantile regression for longitudinal biomarker data subject to left censoring and dropouts

Minjae Lee, Lan Kong

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Quantile regression is increasingly used in biomarker analysis to handle nonnormal or heteroscedastic data. However, in some biomedical studies, the biomarker data can be censored by detection limits of the bioassay or missing when the subjects drop out from the study. Inappropriate handling of these two issues leads to biased estimation results. We consider the censored quantile regression approach to account for the censoring data and apply the inverse weighting technique to adjust for dropouts. In particular, we develop a weighted estimating equation for censored quantile regression, where an individual's contribution is weighted by the inverse probability of dropout at the given occasion. We conduct simulation studies to evaluate the properties of the proposed estimators and demonstrate our method with a real data set from Genetic and Inflammatory Marker of Sepsis (GenIMS) study.

Original languageEnglish (US)
Pages (from-to)4628-4641
Number of pages14
JournalCommunications in Statistics - Theory and Methods
Volume43
Issue number21
DOIs
StatePublished - Nov 15 2014

Fingerprint

Left Censoring
Quantile Regression
Drop out
Biomarkers
Censored Regression
Biased Estimation
Weighted Estimating Equations
Bioassay
Detection Limit
Censoring
Weighting
Simulation Study
Estimator
Evaluate
Demonstrate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this

@article{4de49b16f9494bbb94fc60c444131668,
title = "Quantile regression for longitudinal biomarker data subject to left censoring and dropouts",
abstract = "Quantile regression is increasingly used in biomarker analysis to handle nonnormal or heteroscedastic data. However, in some biomedical studies, the biomarker data can be censored by detection limits of the bioassay or missing when the subjects drop out from the study. Inappropriate handling of these two issues leads to biased estimation results. We consider the censored quantile regression approach to account for the censoring data and apply the inverse weighting technique to adjust for dropouts. In particular, we develop a weighted estimating equation for censored quantile regression, where an individual's contribution is weighted by the inverse probability of dropout at the given occasion. We conduct simulation studies to evaluate the properties of the proposed estimators and demonstrate our method with a real data set from Genetic and Inflammatory Marker of Sepsis (GenIMS) study.",
author = "Minjae Lee and Lan Kong",
year = "2014",
month = "11",
day = "15",
doi = "10.1080/03610926.2012.729641",
language = "English (US)",
volume = "43",
pages = "4628--4641",
journal = "Communications in Statistics - Theory and Methods",
issn = "0361-0926",
publisher = "Taylor and Francis Ltd.",
number = "21",

}

Quantile regression for longitudinal biomarker data subject to left censoring and dropouts. / Lee, Minjae; Kong, Lan.

In: Communications in Statistics - Theory and Methods, Vol. 43, No. 21, 15.11.2014, p. 4628-4641.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Quantile regression for longitudinal biomarker data subject to left censoring and dropouts

AU - Lee, Minjae

AU - Kong, Lan

PY - 2014/11/15

Y1 - 2014/11/15

N2 - Quantile regression is increasingly used in biomarker analysis to handle nonnormal or heteroscedastic data. However, in some biomedical studies, the biomarker data can be censored by detection limits of the bioassay or missing when the subjects drop out from the study. Inappropriate handling of these two issues leads to biased estimation results. We consider the censored quantile regression approach to account for the censoring data and apply the inverse weighting technique to adjust for dropouts. In particular, we develop a weighted estimating equation for censored quantile regression, where an individual's contribution is weighted by the inverse probability of dropout at the given occasion. We conduct simulation studies to evaluate the properties of the proposed estimators and demonstrate our method with a real data set from Genetic and Inflammatory Marker of Sepsis (GenIMS) study.

AB - Quantile regression is increasingly used in biomarker analysis to handle nonnormal or heteroscedastic data. However, in some biomedical studies, the biomarker data can be censored by detection limits of the bioassay or missing when the subjects drop out from the study. Inappropriate handling of these two issues leads to biased estimation results. We consider the censored quantile regression approach to account for the censoring data and apply the inverse weighting technique to adjust for dropouts. In particular, we develop a weighted estimating equation for censored quantile regression, where an individual's contribution is weighted by the inverse probability of dropout at the given occasion. We conduct simulation studies to evaluate the properties of the proposed estimators and demonstrate our method with a real data set from Genetic and Inflammatory Marker of Sepsis (GenIMS) study.

UR - http://www.scopus.com/inward/record.url?scp=84910643456&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84910643456&partnerID=8YFLogxK

U2 - 10.1080/03610926.2012.729641

DO - 10.1080/03610926.2012.729641

M3 - Article

AN - SCOPUS:84910643456

VL - 43

SP - 4628

EP - 4641

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 21

ER -