title: Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.
abstract: Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data.
authors.affiliation: Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: Edmond S-W
authors.initials: ES
authors.lastname: Ng
authors.affiliation: Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: Karla
authors.initials: K
authors.lastname: Diaz-Ordaz
authors.affiliation: Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK richard.grieve@lshtm.ac.uk.
authors.forename: Richard
authors.initials: R
authors.lastname: Grieve
authors.affiliation: Modeling and Simulation Group, Novartis Pharma AG, Basel, Switzerland.
authors.forename: Richard M
authors.initials: RM
authors.lastname: Nixon
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
authors.forename: Simon G
authors.initials: SG
authors.lastname: Thompson
authors.affiliation: Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: James R
authors.initials: JR
authors.lastname: Carpenter
mesh_terms: D016000:Cluster Analysis; D003327:Coronary Disease / Q000517:prevention & control; D003362:Cost-Benefit Analysis / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D016011:Normal Distribution; D019057:Quality-Adjusted Life Years; D016032:Randomized Controlled Trials as Topic / Q000191:economics*; D012107:Research Design; D055502:Secondary Prevention
keywords: cluster randomised trial; cost-effectiveness analyses; multilevel models