Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers

• Published in: Statistics in medicine Vol. 34; no. 10; pp. 1733 - 1746
• Main Authors: Zhao, Lili, Feng, Dai, Neelon, Brian, Buyse, Marc
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 10.05.2015; Wiley Subscription Services, Inc
• Subjects: Antigens; Bayes Theorem; Bayesian analysis; Bayesian hierarchical model; Biomarkers; Biomarkers, Tumor - blood; change point; Clinical trials; Clinical Trials as Topic - statistics & numerical data; Computer Simulation; Disease Progression; Endpoint Determination; Humans; Linear Models; longitudinal data; Longitudinal Studies; Male; Markov Chains; Mathematical functions; Mathematical models; mixture model; Monte Carlo Method; Multicenter Studies as Topic - statistics & numerical data; Prostate cancer; Prostate-Specific Antigen - blood; Prostate-Specific Antigen - drug effects; Prostatic Neoplasms - blood; Prostatic Neoplasms - drug therapy; Prostatic Neoplasms - pathology; PSA; Treatment Outcome; tumor growth profile; Bayesian hierarchical model; mixture model; tumor growth profile; change point; PSA; longitudinal data

Prostate‐specific antigen (PSA) is a widely used marker in clinical trials for patients with prostate cancer. We develop a mixture model to estimate longitudinal PSA trajectory in response to treatment. The model accommodates subjects responding and not responding to therapy through a mixture of two...