Title |
A Comparison of Four Software Programs for Implementing Decision Analytic Cost-Effectiveness Models
|
---|---|
Published in |
PharmacoEconomics, May 2017
|
DOI | 10.1007/s40273-017-0510-8 |
Pubmed ID | |
Authors |
Chase Hollman, Mike Paulden, Petros Pechlivanoglou, Christopher McCabe |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Argentina | 3 | 18% |
Canada | 2 | 12% |
Poland | 1 | 6% |
New Zealand | 1 | 6% |
United States | 1 | 6% |
Colombia | 1 | 6% |
Japan | 1 | 6% |
United Kingdom | 1 | 6% |
Peru | 1 | 6% |
Other | 0 | 0% |
Unknown | 5 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 13 | 76% |
Science communicators (journalists, bloggers, editors) | 2 | 12% |
Scientists | 1 | 6% |
Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 1% |
Unknown | 98 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 22 | 22% |
Student > Ph. D. Student | 16 | 16% |
Student > Master | 10 | 10% |
Student > Doctoral Student | 7 | 7% |
Student > Bachelor | 6 | 6% |
Other | 17 | 17% |
Unknown | 21 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 21 | 21% |
Economics, Econometrics and Finance | 8 | 8% |
Engineering | 8 | 8% |
Pharmacology, Toxicology and Pharmaceutical Science | 8 | 8% |
Social Sciences | 5 | 5% |
Other | 25 | 25% |
Unknown | 24 | 24% |