The Annals of Applied Statistics

Biomarker change-point estimation with right censoring in longitudinal studies

Xiaoying Tang, Michael I. Miller, and Laurent Younes

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Abstract

We consider in this paper a statistical two-phase regression model in which the change point of a disease biomarker is measured relative to another point in time, such as the manifestation of the disease, which is subject to right-censoring (i.e., possibly unobserved over the entire course of the study). We develop point estimation methods for this model, based on maximum likelihood, and bootstrap validation methods. The effectiveness of our approach is illustrated by numerical simulations, and by the estimation of a change point for amygdalar atrophy in the context of Alzheimer’s disease, wherein it is related to the cognitive manifestation of the disease.

Article information

Source
Ann. Appl. Stat. Volume 11, Number 3 (2017), 1738-1762.

Dates
Received: February 2016
Revised: January 2017
First available in Project Euclid: 5 October 2017

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1507168846

Digital Object Identifier
doi:10.1214/17-AOAS1056

Keywords
Change-point estimation right censoring medical imaging

Citation

Tang, Xiaoying; Miller, Michael I.; Younes, Laurent. Biomarker change-point estimation with right censoring in longitudinal studies. Ann. Appl. Stat. 11 (2017), no. 3, 1738--1762. doi:10.1214/17-AOAS1056. https://projecteuclid.org/euclid.aoas/1507168846


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