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 Medjournal_title
Statistics in medicineauthors
Slate EH,Turnbull BWdoi
10.1002/(sici)1097-0258(20000229)19:4<617::aid-simsubject
Has Abstractpub_date
2000-02-29 00:00:00pages
617-37issue
4eissn
0277-6715issn
1097-0258pii
10.1002/(SICI)1097-0258(20000229)19:4<617::AID-SIMjournal_volume
19pub_type
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