Statistical models for longitudinal biomarkers of disease onset.
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.
Slate
E H
EH
School of Operations Research and Industrial Engineering and Department of Statistical Science, Cornell University, Ithaca, NY 14853-3801, USA. slate@orie.cornell.edu
Turnbull
B W
BW
D000465:Algorithms; D001499:Bayes Theorem; D015415:Biomarkers; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D017430:Prostate-Specific Antigen; D011471:Prostatic Neoplasms / Q000175:diagnosis; D012372:ROC Curve
D015415:Biomarkers; D017430:Prostate-Specific Antigen