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A Comparison of Four Software Programs for Implementing Decision Analytic Cost-Effectiveness Models

Author

Listed:
  • Chase Hollman

    (University of Alberta)

  • Mike Paulden

    (University of Alberta
    University of Alberta)

  • Petros Pechlivanoglou

    (The Hospital for Sick Children
    University of Toronto
    University of Toronto)

  • Christopher McCabe

    (University of Alberta)

Abstract

The volume and technical complexity of both academic and commercial research using decision analytic modelling has increased rapidly over the last two decades. The range of software programs used for their implementation has also increased, but it remains true that a small number of programs account for the vast majority of cost-effectiveness modelling work. We report a comparison of four software programs: TreeAge Pro, Microsoft Excel, R and MATLAB. Our focus is on software commonly used for building Markov models and decision trees to conduct cohort simulations, given their predominance in the published literature around cost-effectiveness modelling. Our comparison uses three qualitative criteria as proposed by Eddy et al.: “transparency and validation”, “learning curve” and “capability”. In addition, we introduce the quantitative criterion of processing speed. We also consider the cost of each program to academic users and commercial users. We rank the programs based on each of these criteria. We find that, whilst Microsoft Excel and TreeAge Pro are good programs for educational purposes and for producing the types of analyses typically required by health technology assessment agencies, the efficiency and transparency advantages of programming languages such as MATLAB and R become increasingly valuable when more complex analyses are required.

Suggested Citation

  • Chase Hollman & Mike Paulden & Petros Pechlivanoglou & Christopher McCabe, 2017. "A Comparison of Four Software Programs for Implementing Decision Analytic Cost-Effectiveness Models," PharmacoEconomics, Springer, vol. 35(8), pages 817-830, August.
    • Handle: RePEc:spr:pharme:v:35:y:2017:i:8:d:10.1007_s40273-017-0510-8
    DOI: 10.1007/s40273-017-0510-8
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    References listed on IDEAS

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    1. Mark Sculpher & Michael Drummond & Martin Buxton, 1995. "Economic Evaluation in Health Care Research and Development: Undertake it Early and Often," Discussion Papers 12, Brunel University, Health Economics Research Group.
    2. Hawre Jalal & Petros Pechlivanoglou & Eline Krijkamp & Fernando Alarid-Escudero & Eva Enns & M. G. Myriam Hunink, 2017. "An Overview of R in Health Decision Sciences," Medical Decision Making, , vol. 37(7), pages 735-746, October.
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    Cited by:

    1. Eline M. Krijkamp & Fernando Alarid-Escudero & Eva A. Enns & Hawre J. Jalal & M. G. Myriam Hunink & Petros Pechlivanoglou, 2018. "Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial," Medical Decision Making, , vol. 38(3), pages 400-422, April.
    2. Narinder Singh & Francesco Colangelo & Ilenia Farina, 2023. "Sustainable Non-Conventional Concrete 3D Printing—A Review," Sustainability, MDPI, vol. 15(13), pages 1-42, June.

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