Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.
Ng, Edmond S-W
Nixon, Richard M
Carpenter, James R
Stat Methods Med Res
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Ng, E. S., Diaz-Ordaz, K., Grieve, R., Nixon, R. M., Thompson, S., & Carpenter, J. R. (2016). Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.. Stat Methods Med Res, 25 (5), 2036-2052. https://doi.org/10.1177/0962280213511719
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.
Humans, Coronary Disease, Cluster Analysis, Monte Carlo Method, Markov Chains, Normal Distribution, Quality-Adjusted Life Years, Research Design, Cost-Benefit Analysis, Randomized Controlled Trials as Topic, Secondary Prevention
Medical Research Council (MR/L003120/1)
British Heart Foundation (None)
External DOI: https://doi.org/10.1177/0962280213511719
This record's URL: https://www.repository.cam.ac.uk/handle/1810/269350