NETWORK META-ANALYSIS OF DIAGNOSTIC TEST ACCURACY STUDIES ALLOWING FOR MULTIPLE TESTS AT MULTIPLE THRESHOLDS FOR HEALTHCARE POLICY AND DECISION MAKING

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

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): Data sources and methods to construct a population-based research platform to investigate multimorbidity

Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity. Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals