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Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making
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Nicola J. Cooper,
Terence J. Quinn,
Rosalind Lees,
Alex J. Sutton
Journal of Clinical Epidemiology, Volume: 99, Pages: 64 - 74
Swansea University Author:
Rhiannon Owen
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Copyright: 2018 The Authors. This is an open access article under the CC BY-NC-ND license
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DOI (Published version): 10.1016/j.jclinepi.2018.03.005
Abstract
ObjectivesNetwork meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are...
| Published in: | Journal of Clinical Epidemiology |
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| ISSN: | 0895-4356 |
| Published: |
Elsevier BV
2018
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60670 |
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| Abstract: |
ObjectivesNetwork meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, 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, in one simultaneous analysis.Study Design and SettingMotivated 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 incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study.ResultsWe developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whereas MMSE at threshold <25/30 appeared to have the best true negative rate.ConclusionThe combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making. |
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| Keywords: |
Network meta-analysis; Meta-analysis; Diagnostic test accuracy; Multiple tests; Multiple thresholds |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Start Page: |
64 |
| End Page: |
74 |