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Mix and match. A simulation study on the impact of mixed-treatment comparison methods on health-economic outcomes

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      Abstract

      BackgroundDecision-analytic cost-effectiveness (CE) models combine many parameters, often obtained after meta-analysis.AimWe compared different methods of mixed-treatment comparison (MTC) to combine transition and event probabilities derived from several trials, especially with respect to health-economic (HE) outcomes like (quality adjusted) life years and costs.MethodsTrials were drawn from a simulated reference population, comparing two of four fictitious interventions. The goal was to estimate the CE between two of these. The amount of heterogeneity between trials was varied in scenarios. Parameter estimates were combined using direct comparison, MTC methods proposed by Song and Puhan, and Bayesian generalized linear fixed effects (GLMFE) and random effects models (GLMRE). Parameters were entered into a Markov model. Parameters and HE outcomes were compared with the reference population using coverage, statistical power, bias and mean absolute deviation (MAD) as performance indicators. Each analytical step was repeated 1,000 times.ResultsThe direct comparison was outperformed by the MTC methods on all indicators, Song’s method yielded low bias and MAD, but uncertainty was overestimated. Puhan’s method had low bias and MAD and did not overestimate uncertainty. GLMFE generally had the lowest bias and MAD, regardless of the amount of heterogeneity, but uncertainty was overestimated. GLMRE showed large bias and MAD and overestimated uncertainty. Song’s and Puhan’s methods lead to the least amount of uncertainty, reflected in the shape of the CE acceptability curve. GLMFE showed slightly more uncertainty.ConclusionsCombining direct and indirect evidence is superior to using only direct evidence. Puhan’s method and GLMFE are preferred.

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      Measuring inconsistency in meta-analyses.

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        Meta-analysis in clinical trials.

        This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.
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          Combination of direct and indirect evidence in mixed treatment comparisons.

          Mixed treatment comparison (MTC) meta-analysis is a generalization of standard pairwise meta-analysis for A vs B trials, to data structures that include, for example, A vs B, B vs C, and A vs C trials. There are two roles for MTC: one is to strengthen inference concerning the relative efficacy of two treatments, by including both 'direct' and 'indirect' comparisons. The other is to facilitate simultaneous inference regarding all treatments, in order for example to select the best treatment. In this paper, we present a range of Bayesian hierarchical models using the Markov chain Monte Carlo software WinBUGS. These are multivariate random effects models that allow for variation in true treatment effects across trials. We consider models where the between-trials variance is homogeneous across treatment comparisons as well as heterogeneous variance models. We also compare models with fixed (unconstrained) baseline study effects with models with random baselines drawn from a common distribution. These models are applied to an illustrative data set and posterior parameter distributions are compared. We discuss model critique and model selection, illustrating the role of Bayesian deviance analysis, and node-based model criticism. The assumptions underlying the MTC models and their parameterization are also discussed. Copyright 2004 John Wiley & Sons, Ltd.
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            Author and article information

            Affiliations
            Institute for Medical Technology Assessment (iMTA), Erasmus University, Rotterdam, The Netherlands
            Public Library of Science, FRANCE
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            • Conceived and designed the experiments: PV MA MO MR.

            • Performed the experiments: PV.

            • Analyzed the data: PV MA MO MR.

            • Contributed reagents/materials/analysis tools: PV MO.

            • Wrote the paper: PV MA MO MR.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            2 February 2017
            2017
            : 12
            : 2
            28152099
            5289594
            10.1371/journal.pone.0171292
            PONE-D-15-24500
            (Editor)
            © 2017 Vemer et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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            Figures: 5, Tables: 5, Pages: 20
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            Funding
            Funded by: Netherlands Organization for Health Research and Development (ZonMW)
            Award ID: 152002002
            Award Recipient :
            This study was financially supported by the Netherlands Organization for Health Research and Development (ZonMW, http://www.zonmw.nl/nl/projecten/project-detail/updating-parameters-of-decision-analytic-cost-effectiveness-models-a-systematic-comparison-of-metho/voortgang/), project number 152002002. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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            Research and Analysis Methods
            Mathematical and Statistical Techniques
            Statistical Methods
            Meta-Analysis
            Physical Sciences
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            Meta-Analysis
            Research and Analysis Methods
            Simulation and Modeling
            Research and Analysis Methods
            Database and Informatics Methods
            Health Informatics
            Social Sciences
            Economics
            Economic Analysis
            Cost-Effectiveness Analysis
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            Mathematical and Statistical Techniques
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            Generalized Linear Model
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