Quantile regression on inactivity time

Lauren C. Balmert*, Ruosha Li, Limin Peng, Jong Hyeon Jeong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a semi-parametric regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance–covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer.

Original languageEnglish (US)
Pages (from-to)1332-1346
Number of pages15
JournalStatistical Methods in Medical Research
Volume30
Issue number5
DOIs
StatePublished - May 2021

Keywords

  • : Censoring
  • Donsker’s class
  • lost lifespan
  • perturbation
  • time-to-event data

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Fingerprint

Dive into the research topics of 'Quantile regression on inactivity time'. Together they form a unique fingerprint.

Cite this