Department of Emergency Medicine, University of Alberta, 3-264 Edmonton Clinic Health Academy, 11405 87 Ave NW, Edmonton, AB, T6G 1C9, Canada.
School of Public Health, University of Alberta, Edmonton, Canada.
Pharmacoeconomics. 2017 Aug;35(8):817-830. doi: 10.1007/s40273-017-0510-8.
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.
在过去的二十年中，使用决策分析模型进行学术和商业研究的数量和技术复杂性迅速增加。用于实施这些模型的软件程序的范围也有所增加，但实际上，少数程序占据了绝大多数成本效益建模工作。我们报告了对四个软件程序的比较：TreeAge Pro、Microsoft Excel、R 和 MATLAB。我们的重点是常用于构建 Markov 模型和决策树以进行队列模拟的软件，因为它们在成本效益建模的已发表文献中占主导地位。我们的比较使用 Eddy 等人提出的三个定性标准：“透明度和验证”、“学习曲线”和“能力”。此外，我们引入了处理速度的定量标准。我们还考虑了每个程序对学术用户和商业用户的成本。我们根据这些标准对程序进行排名。我们发现，虽然 Microsoft Excel 和 TreeAge Pro 是用于教育目的和制作健康技术评估机构通常需要的类型分析的好程序，但当需要更复杂的分析时，编程语言（如 MATLAB 和 R）的效率和透明度优势变得越来越有价值。
