Statistical models for longitudinal biomarkers of disease onset

• Published in: Statistics in medicine 2000-02, Vol.19 (4), p.617-637
• Main Authors: Slate, Elizabeth H., Turnbull, Bruce W.
• Format: Article
• Language: English
• Subjects: Algorithms; Bayes Theorem; Biological and medical sciences; Biomarkers; Computerized, statistical medical data processing and models in biomedicine; Humans; Longitudinal Studies; Male; Medical sciences; Medical statistics; Models, Statistical; Prostate-Specific Antigen; Prostatic Neoplasms - diagnosis; ROC Curve
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/(SICI)1097-0258(20000229)19:4<617::AID-SIM360>3.0.CO;2-R

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. Copyright © 2000 John Wiley & Sons, Ltd.