Statistical models for longitudinal biomarkers of disease onset.

Abstract:

:We consider the analysis of serial biomarkers to screen and monitor individuals in a given population for onset of a specific disease of interest. The biomarker readings are subject to error. We survey some of the existing literature and concentrate on two recently proposed models. The first is a fully Bayesian hierarchical structure for a mixed effects segmented regression model. Posterior estimates of the changepoint (onset time) distribution are obtained by Gibbs sampling. The second is a hidden changepoint model in which the onset time distribution is estimated by maximum likelihood using the EM algorithm. Both methods lead to a dynamic index that represents a strength of evidence that onset has occurred by the current time in an individual subject. The methods are applied to some large data sets concerning prostate specific antigen (PSA) as a serial marker for prostate cancer. Rules based on the indices are compared to standard diagnostic criteria through the use of ROC curves adapted for longitudinal data.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Slate EH,Turnbull BW

doi

10.1002/(sici)1097-0258(20000229)19:4<617::aid-sim

subject

Has Abstract

pub_date

2000-02-29 00:00:00

pages

617-37

issue

4

eissn

0277-6715

issn

1097-0258

pii

10.1002/(SICI)1097-0258(20000229)19:4<617::AID-SIM

journal_volume

19

pub_type

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