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

Author

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  • Pepijn Vemer
  • Maiwenn J Al
  • Mark Oppe
  • Maureen P M H Rutten-van Mölken

Abstract

Background: Decision-analytic cost-effectiveness (CE) models combine many parameters, often obtained after meta-analysis. Aim: We 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. Methods: Trials 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. Results: The 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. Conclusions: Combining direct and indirect evidence is superior to using only direct evidence. Puhan’s method and GLMFE are preferred.

Suggested Citation

  • Pepijn Vemer & Maiwenn J Al & Mark Oppe & Maureen P M H Rutten-van Mölken, 2017. "Mix and match. A simulation study on the impact of mixed-treatment comparison methods on health-economic outcomes," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-20, February.
  • Handle: RePEc:plo:pone00:0171292
    DOI: 10.1371/journal.pone.0171292
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    References listed on IDEAS

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    1. Lu, Guobing & Ades, A.E., 2006. "Assessing Evidence Inconsistency in Mixed Treatment Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 447-459, June.
    2. Sofia Dias & Nicky J. Welton & Alex J. Sutton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 5," Medical Decision Making, , vol. 33(5), pages 657-670, July.
    3. Sofia Dias & Alex J. Sutton & A. E. Ades & Nicky J. Welton, 2013. "Evidence Synthesis for Decision Making 2," Medical Decision Making, , vol. 33(5), pages 607-617, July.
    4. Pepijn Vemer & Maiwenn Al & Mark Oppe & Maureen Rutten-van Mölken, 2013. "A Choice That Matters?," PharmacoEconomics, Springer, vol. 31(8), pages 719-730, August.
    5. Mark Oppe & Maiwenn Al & Maureen Rutten-Mölken, 2011. "Comparing Methods of Data Synthesis," PharmacoEconomics, Springer, vol. 29(3), pages 239-250, March.
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