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Stat Med. 2019 Oct 30;38(24):4789-4803. doi: 10.1002/sim.8301. Epub 2019 Sep 30.

Quantifying how diagnostic test accuracy depends on threshold in a meta-analysis.

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Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Department of Biostatistics, Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island.
Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, Rhode Island.


Tests for disease often produce a continuous measure, such as the concentration of some biomarker in a blood sample. In clinical practice, a threshold C is selected such that results, say, greater than C are declared positive and those less than C negative. Measures of test accuracy such as sensitivity and specificity depend crucially on C, and the optimal value of this threshold is usually a key question for clinical practice. Standard methods for meta-analysis of test accuracy (i) do not provide summary estimates of accuracy at each threshold, precluding selection of the optimal threshold, and furthermore, (ii) do not make use of all available data. We describe a multinomial meta-analysis model that can take any number of pairs of sensitivity and specificity from each study and explicitly quantifies how accuracy depends on C. Our model assumes that some prespecified or Box-Cox transformation of test results in the diseased and disease-free populations has a logistic distribution. The Box-Cox transformation parameter can be estimated from the data, allowing for a flexible range of underlying distributions. We parameterise in terms of the means and scale parameters of the two logistic distributions. In addition to credible intervals for the pooled sensitivity and specificity across all thresholds, we produce prediction intervals, allowing for between-study heterogeneity in all parameters. We demonstrate the model using two case study meta-analyses, examining the accuracy of tests for acute heart failure and preeclampsia. We show how the model can be extended to explore reasons for heterogeneity using study-level covariates.


Box-Cox transformation; ROC curve; evidence synthesis; sensitivity; specificity; test cutoff


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