A tutorial on dynamic risk prediction of a binary outcome based on a longitudinal biomarker.

ABSTRACT: Dynamic risk predictions based on all available information are useful in timely identification of high-risk patients. However, in contrast with time to event outcomes, there is still a lack of studies that clearly demonstrate how to obtain and update predictions for a future binary outcome using a repeatedly measured biomarker. The aim of this study is to give an illustrative overview of four approaches to obtain such predictions: likelihood based two-stage method (2SMLE), likelihood based joint model (JMMLE), Bayesian two-stage method (2SB), and Bayesian joint model (JMB). We applied the approaches to provide weekly updated predictions of post-molar gestational trophoblastic neoplasia (GTN) based on age and repeated measurements of human chorionic gonadotropin (hCG). Discrimination and c

SUBMITTER: Dandis R 

PROVIDER: S-EPMC7079044 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature