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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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.

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

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

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Change-point estimation right censoring medical imaging


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.

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