Skip to content

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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)4789-4803
Number of pages15
JournalStatistics in Medicine
Volume38
Issue number24
Early online date30 Sep 2019
DOIs
DateAccepted/In press - 7 Jun 2019
DateE-pub ahead of print - 30 Sep 2019
DatePublished (current) - 30 Oct 2019

Abstract

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.

    Research areas

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

Download statistics

No data available

Documents

Documents

  • Full-text PDF (final published version)

    Rights statement: This is the final published version of the article (version of record). It first appeared online via Wiley at https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8301. Please refer to any applicable terms of use of the publisher.

    Final published version, 4 MB, PDF document

    Licence: CC BY

DOI

View research connections

Related faculties, schools or groups