Statistical models for longitudinal biomarkers of disease onset

✍ Scribed by Elizabeth H. Slate; Bruce W. Turnbull

• Book ID: 101239373
• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 213 KB
• Volume: 19
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(20000229)19:4<617::aid-sim360>3.0.co;2-r

✦ Synopsis

We consider the analysis of serial biomarkers to screen and monitor individuals in a given population for onset of a speci"c 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 "rst is a fully Bayesian hierarchical structure for a mixed e!ects 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 speci"c 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.