Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/43828
Title: NETWORK META-ANALYSIS OF DIAGNOSTIC TEST ACCURACY STUDIES ALLOWING FOR MULTIPLE TESTS AT MULTIPLE THRESHOLDS FOR HEALTHCARE POLICY AND DECISION MAKING
Authors: Owen, R
Cooper, NJ
Quinn, T
Sutton, A
First Published: 24-Dec-2018
Publisher: Elsevier for International Society for Pharmacoeconomics and Outcomes Research
Citation: Value in Health, 2018, 21, pp. S10-S10 (1)
Abstract: Objectives Network meta-analyses have extensively been used to compare the effectiveness of multiple interventions for healthcare policy and decision-making. Methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds. The aim of this research was to develop a network meta-analysis framework for evaluating multiple diagnostic tests, at varying test thresholds in one simultaneous analysis. Methods Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov Chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model, accounting for the correlations between multiple test accuracy measures from the same study, and incorporating constraints on increasing test thresholds assuming that higher test thresholds had an increased sensitivity but decreased specificity. Results We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Applying constraints on increasing test thresholds reduced the within-study variability and increased the precision in estimates of sensitivity and specificity. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate (estimated sensitivity: 0.98; 95% credible interval (CrI): 0.94,0.99), whilst MMSE at threshold <25/30 appeared to have the best true negative rate (estimated specificity: 0.84, 95%CrI: 0.79,0.88). Conclusions In a health technology assessment setting, there is an increasing need to compare the efficiency of multiple diagnostics tests. The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision-making.
DOI Link: 10.1016/j.jval.2018.09.057
ISSN: 1098-3015
eISSN: 1524-4733
Links: https://www.sciencedirect.com/science/article/pii/S1098301518333576?via%3Dihub
http://hdl.handle.net/2381/43828
Embargo on file until: 24-Dec-2019
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © Elsevier 2018. After an embargo period this version of the paper will be an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Description: The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Conference Papers & Presentations, Dept. of Health Sciences

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