Quantile regression on inactivity time.
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 be...
| Publicado en: | Statistical Methods in Medical Research Vol. 30; no. 5; pp. 1332 - 1347 |
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| Autores principales: | , , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
May2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150365443&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150365443 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: May2021 vid: 30 iid: 5 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 150365443 149360683 150365443 NLM33749407 150365443 10.1177/0962280221995977 NLM33749407 150365443 ppf: 1332 ppct: 15 formats: tig: atl: Quantile regression on inactivity time. aug: au: Balmert, Lauren C Li, Ruosha Peng, Limin Jeong, Jong-Hyeon affil: Department of Preventive Medicine (Biostatistics), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA sug: subj: Study Design Breast Neoplasms Computer Simulation Regression Female Funding Source Female ab: 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. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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