Academic Journal

Non-parametric estimation of the covariate-dependent bivariate distribution for censored gap times

Bibliographic Details
Title: Non-parametric estimation of the covariate-dependent bivariate distribution for censored gap times
Authors: Strzalkowska-Kominiak, Ewa, Molanes-López, Elisa M., Letón, Emilio
Source: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Publisher Information: Institut d'Estadística de Catalunya, 2024.
Publication Year: 2024
Subject Terms: Estadística matemàtica, Classificació AMS::62 Statistics::62N Survival analysis and censored data, serial dependence, random censoring, bivariate distribution, copula function, Classificació AMS::62 Statistics::62P Applications, Mathematical statistics, covariate, Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica, kernel estimation, Classificació AMS::62 Statistics::62G Nonparametric inference
Description: In many biomedical studies, recurrent or consecutive events may occur during the follow up of the individuals. This situation can be found, for example, in transplant studies, where there are two consecutive events which give rise to two times of interest subject to a common random right-censoring time, the first one being the elapsed time from acceptance into the transplantation program to transplant, and the second one the time from transplant to death. In this work, we incorporate the information of a continuous covariate into the bivariate distribution of the two gap times of interest and propose a non-parametric method to cope with it. We prove the asymptotic properties of the proposed method and carry out a simulation study to see the performance of this approach. Additionally, we illustrate its use with Stanford heart transplant data and colon cancer data.
Document Type: Article
File Description: application/pdf
Language: English
DOI: 10.57645/20.8080.02.18
Rights: CC BY NC ND
Accession Number: edsair.dedup.wf.002..01bca2b1a672402538a8be70ccb37eb1
Database: OpenAIRE
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