Rights statement: The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 28 (8), 2018, © SAGE Publications Ltd, 2018 by SAGE Publications Ltd at the Statistical Methods in Medical Research page: http://journals.sagepub.com/home/smm on SAGE Journals Online: http://journals.sagepub.com/
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A Bayesian model to estimate the cutoff and the clinical utility of a biomarker assay
AU - Vradi, Eleni
AU - Jaki, Thomas Friedrich
AU - Vonk, Richardus
AU - Brannath, Werner
N1 - The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 28 (8), 2018, © SAGE Publications Ltd, 2018 by SAGE Publications Ltd at the Statistical Methods in Medical Research page: http://journals.sagepub.com/home/smm on SAGE Journals Online: http://journals.sagepub.com/
PY - 2019/8/1
Y1 - 2019/8/1
N2 - To enable targeted therapies and enhance medical decision-making, biomarkers are increasingly used as screening and diagnostic tests. When using quantitative biomarkers for classification purposes, this often implies that an appropriate cutoff for the biomarker has to be determined and its clinical utility must be assessed. In the context of drug development, it is of interest how the probability of response changes with increasing values of the biomarker. Unlike sensitivity and specificity, predictive values are functions of the accuracy of the test, depend on the prevalence of the disease and therefore are a useful tool in this setting. In this paper, we propose a Bayesian method to not only estimate the cutoff value using the negative and positive predictive values, but also estimate the uncertainty around this estimate. Using Bayesian inference allows us to incorporate prior information, and obtain posterior estimates and credible intervals for the cut-off and associated predictive values. The performance of the Bayesian approach is compared with alternative methods via simulation studies of bias, interval coverage and width and illustrations on real data with binary and time-to-event outcomes are provided.
AB - To enable targeted therapies and enhance medical decision-making, biomarkers are increasingly used as screening and diagnostic tests. When using quantitative biomarkers for classification purposes, this often implies that an appropriate cutoff for the biomarker has to be determined and its clinical utility must be assessed. In the context of drug development, it is of interest how the probability of response changes with increasing values of the biomarker. Unlike sensitivity and specificity, predictive values are functions of the accuracy of the test, depend on the prevalence of the disease and therefore are a useful tool in this setting. In this paper, we propose a Bayesian method to not only estimate the cutoff value using the negative and positive predictive values, but also estimate the uncertainty around this estimate. Using Bayesian inference allows us to incorporate prior information, and obtain posterior estimates and credible intervals for the cut-off and associated predictive values. The performance of the Bayesian approach is compared with alternative methods via simulation studies of bias, interval coverage and width and illustrations on real data with binary and time-to-event outcomes are provided.
KW - Bayesian model
KW - cutoff estimation
KW - predictive values
KW - step function
KW - diagnostic tests
KW - clinical utility
KW - response rates
U2 - 10.1177/0962280218784778
DO - 10.1177/0962280218784778
M3 - Journal article
VL - 28
SP - 2538
EP - 2556
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
IS - 8
ER -