A Bayesian method to estimate the optimal threshold of a longitudinal biomarker.
The objective of this study was to develop methods to estimate the optimal threshold of a longitudinal biomarker and its credible interval when the diagnostic test is based on a criterion that reflects a dynamic progression of that biomarker. Two methods are proposed: one parametric and one non-parametric. In both the cases, the Bayesian inference was used to derive the posterior distribution of the optimal threshold from which an estimate and a credible interval could be obtained. A numerical study shows that the bias of the parametric method is low and the coverage probability of the credible interval close to the nominal value, with a small coverage asymmetry in some cases. This is also true for the non-parametric method in case of large sample sizes. Both the methods were applied to estimate the optimal prostate-specific antigen nadir value to diagnose prostate cancer recurrence after a high-intensity focused ultrasound treatment. The parametric method can also be applied to non-longitudinal biomarkers.
Université de Lyon, France. fabien.subtil@chu-lyon.fr <fabien.subtil@chu-lyon.fr>
Fabien
F
Subtil
Muriel
M
Rabilloud
D001499:Bayes Theorem; D015415:Biomarkers / Q000378:metabolism; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008954:Models, Biological; D017430:Prostate-Specific Antigen / Q000378:metabolism*; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000453:epidemiology / Q000378:metabolism; D012008:Recurrence
D015415:Biomarkers; D017430:Prostate-Specific Antigen