Statistical Methods for Cost-Effectiveness Analyses That Use Data from Cluster Randomized Trials: A Systematic Review and Checklist for Critical Appraisal.


Gomes, M; Grieve, R; Nixon, R; Edmunds, WJ; (2012) Statistical Methods for Cost-Effectiveness Analyses That Use Data from Cluster Randomized Trials: A Systematic Review and Checklist for Critical Appraisal. Medical decision making, 32 (1). pp. 209-20. ISSN 0272-989X DOI: https://doi.org/10.1177/0272989X11407341

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Abstract

INTRODUCTION: The best data for cost-effectiveness analyses (CEAs) of group-level interventions often come from cluster randomized trials (CRTs), where randomization is by cluster (e.g., the hospital attended), not by individual.<br/> METHODS: for these CEAs need to recognize both the correlation between costs and outcomes and that these data may be dependent on the cluster. General checklists and methodological guidance for critically appraising CEA ignore these issues. This article develops a new checklist and applies it in a systematic review of CEAs that use CRTs.<br/> METHODS: The authors developed a checklist for CEAs that use CRTs, informed by a conceptual review of statistical methods. This checklist included criteria such as whether the analysis allowed for both clustering and the correlation between individuals' costs and outcomes. The authors undertook a systematic literature review of full economic evaluations that used CRTs. The quality of studies was assessed with the new checklist and by the "Drummond checklist."<br/> RESULTS: The authors identified 62 papers that met the inclusion criteria. On average, studies satisfied 9 of the 10 criteria for the checklist but only 20% of criteria for the new checklist. More than 40% of studies adopted statistical methods that completely ignored clustering, and 75% disregarded any correlation between costs and outcomes. Only 4 studies employed appropriate statistical methods that allowed for both clustering and correlation.<br/> CONCLUSIONS: Most economic evaluations that use data from CRTs ignored clustering or correlation. Statistical methods that address these issues are available, and their use should be encouraged. The new checklist can supplement generic CEA guidelines and highlight where research practice can be improved.<br/>

Item Type: Article
Faculty and Department: Faculty of Public Health and Policy > Dept of Health Services Research and Policy
Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Research Centre: Centre for Statistical Methodology
PubMed ID: 21610256
Web of Science ID: 299701100023
URI: http://researchonline.lshtm.ac.uk/id/eprint/607

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