Rhiannon K Owen1, Nicola J Cooper2, Terence J Quinn3, Rosalind Lees3, Alex J Sutton2. 1. Department of Health Sciences, University of Leicester, Leicester, UK. Electronic address: Rhiannon.owen@le.ac.uk. 2. Department of Health Sciences, University of Leicester, Leicester, UK. 3. Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
Abstract
OBJECTIVES: Network 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 SETTING: 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 incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. RESULTS: We 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. CONCLUSION: The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making.
OBJECTIVES: Network 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 SETTING: 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 incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. RESULTS: We 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. CONCLUSION: The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making.
Authors: Terence J Quinn; Edo Richard; Yvonne Teuschl; Thomas Gattringer; Melanie Hafdi; John T O'Brien; Niamh Merriman; Celine Gillebert; Hanne Huyglier; Ana Verdelho; Reinhold Schmidt; Emma Ghaziani; Hysse Forchammer; Sarah T Pendlebury; Rose Bruffaerts; Milija Mijajlovic; Bogna A Drozdowska; Emily Ball; Hugh S Markus Journal: Eur Stroke J Date: 2021-10-08
Authors: Lucy C Beishon; Emma Elliott; Tuuli M Hietamies; Riona Mc Ardle; Aoife O'Mahony; Amy R Elliott; Terry J Quinn Journal: Cochrane Database Syst Rev Date: 2022-04-08
Authors: Kirsty Hendry; Claire Green; Rupert McShane; Anna H Noel-Storr; David J Stott; Sumayya Anwer; Alex J Sutton; Jennifer K Burton; Terry J Quinn Journal: Cochrane Database Syst Rev Date: 2019-03-04