Meta-Analysis of Test Accuracy Studies with Multiple and Missing Thresholds: A Multivariate-Normal ModelRichard D Riley1*, Yemisi Takwoingi1, Thomas Trikalinos2, Apratim Guha3, Atanu Biswas4, Joie Ensor1, R Katie Morris5,6 and Jonathan J Deeks1
2Center for Evidence-based Medicine, Center for Statistical Sciences, and Department of Health Services, Policy & Practice, Brown University School of Public Health, Brown University, Providence, RI 02912, USA
- *Corresponding Author:
- Richard D. Riley
School of Health and Population Sciences
Public Health Building, University of Birmingham
Edgbaston, Birmingham, B15 2TT, UK
Tel: 0121 414 7508
Fax: 0121 414 7878
E-mail: [email protected]
Received date: April 04, 2014; Accepted date: May 15, 2014; Published date: May 20, 2014
Citation: Riley RD, Takwoingi Y, Trikalinos T, Guha A, Biswas A, et al. (2014) Meta-Analysis of Test Accuracy Studies with Multiple and Missing Thresholds: A Multivariate-Normal Model. J Biomet Biostat 5:196. doi:10.4172/2155-6180.1000196
Copyright: © 2014 Riley RD, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.
Background: When meta-analysing studies examining the diagnostic/predictive accuracy of classifications based on a continuous test, each study may provide results for one or more thresholds, which can vary across studies. Researchers typically meta-analyse each threshold independently. We consider a multivariate meta-analysis to synthesise results for all thresholds simultaneously and account for their correlation.
Methods: We assume that the logit sensitivity and logit specificity estimates follow a multivariate-normal distribution within studies. We model the true logit sensitivity (logit specificity) as monotonically decreasing (increasing) functions of the continuous threshold. This produces a summary ROC curve, a summary estimate of sensitivity and specificity for each threshold, and reveals the heterogeneity in test accuracy across studies. Application is made to 13 studies of protein:creatinine ratio (PCR) for detecting significant proteinuria in pregnancy that each report up to nine thresholds, with 23 distinct thresholds across studies.
Results: In the example there were large within-study and between-study correlations, which were accounted for by the method. A cubic relationship on the logit scale was a better fit for the summary ROC curve than a linear or quadratic one. Between-study heterogeneity was substantial. Based on the summary ROC curve, a PCR value of 0.30 to 0.35 corresponded to maximal pair of summary sensitivity and specificity. Limitations of the proposed model include the need to posit parametric functions for the relationship of sensitivity and specificity with the threshold, to ensure correct ordering of summary threshold results, and the multivariate-normal approximation to the within-study sampling distribution. Conclusion: The joint analysis of test performance data reported over multiple thresholds is feasible. The proposed approach handles different sets of available thresholds per study, and produces a summary ROC curve and summary results for each threshold to inform decision-making.