Improving Transparency in Decision Models: Current Issues and Potential Solutions

Tappenden, Paul; Jaime, J.

doi:10.1007/s40273-019-00850-0

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Transparency can be defined as provision of documentation on a model's structure, equations, parameter values and assumptions. To be fully transparent, the model needs to be understandable to both non-modellers and modellers who may want to replicate the model and findings [16,17]. Models implemented using R are transparent in ways unfamiliar to Excel.…”

Section: Comparative Transparency Of R Versus Excelmentioning

confidence: 99%

R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

et al. 2020

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Introduction: Health economics models are typically built in Microsoft Excel ® owing to its wide familiarity, accessibility and perceived transparency. However, given the increasingly rapid and analytically complex decision-making needs of both the pharmaceutical industry and the field of health economics and outcomes research (HEOR), the demands of cost-effectiveness analyses may be better met by the programming language R.Objective: This case study provides an explicit comparison between Excel and R for contemporary cost-effectiveness analysis. Methods:We constructed duplicate cost-effectiveness models using Excel and R (with a user interface built using the Shiny package) to address a hypothetical case study typical of contemporary health technology assessment. Results:We compared R and Excel versions of the same model design to determine the advantages and limitations of the modelling platforms in terms of (i) analytical capability, (ii) data safety, (iii) building considerations, (iv) usability for technical and non-technical users and (v) model adaptability. Conclusions:The findings of this explicit comparison are used to produce recommendations for when R might be more suitable than Excel in contemporary cost-effectiveness analyses. We conclude that selection of appropriate modelling software needs to consider case-by-case modelling requirements, particularly (i) intended audience, (ii) complexity of analysis, (iii) nature and frequency of updates and (iv) anticipated model run-time.

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Section: Comparative Transparency Of R Versus Excelmentioning

confidence: 99%

R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

et al. 2020

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Validating Health Economic Models With the Probabilistic Analysis Check dashBOARD

Pouwels,

Kroeze,

van der Linden

et al. 2024

Value in Health

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Development of a pharmacoeconomic registry: an example using hormonal contraceptives

et al. 2021

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Background Disease-specific registries, documenting costs and probabilities from pharmacoeconomic studies along with health state utility values from quality-of-life studies could serve as a resource to guide researchers in evaluating the published literature and in the conduct of future economic evaluations for their own research. Registries cataloging economic evaluations currently exist, however they are restricted by the type of economic evaluations they include. There is a need for intervention-specific registries, that document all types of complete and partial economic evaluations and auxiliary information such as quality of life studies. The objective of this study is to describe the development of a pharmacoeconomic registry and provide best practices using an example of hormonal contraceptives. Methods An expert panel consisting of researchers with expertise in pharmacoeconomics and outcomes research was convened and the clinical focus of the registry was finalized after extensive discussion. A list of key continuous, categorical and descriptive variables was developed to capture all relevant data with each variable defined in a data dictionary. A web-based data collection tool was designed to capture and store the resulting metadata. A keyword based search strategy was developed to retrieve the published sources of literature. Finally, articles were screened for relevancy and data was extracted to populate the registry. Expert opinions were taken from the panel at each stage to arrive at consensus and ensure validity of the registry. Results The registry focused on economic evaluation literature of hormonal contraceptives used for contraception. The registry consisted of 65 articles comprising of 22 cost-effectiveness analyses, 9 cost-utility analyses, 7 cost-benefit analyses, 1 cost-minimization, 14 cost analyses, 10 cost of illness studies and 2 quality of life studies. The best practices followed in the development of the registry were summarized as recommendations. The completed registry, data dictionary and associated data files can be accessed in the supplementary information files. Conclusion This registry is a comprehensive database of economic evaluations, including costs, clinical probabilities and health-state utility estimates. The collated data captured from published information in this registry can be used to identify trends in the literature, conduct systematic reviews and meta-analysis and develop novel pharmacoeconomic models.

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Improving Transparency in Decision Models: Current Issues and Potential Solutions

Cited by 4 publications

References 11 publications

R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

Validating Health Economic Models With the Probabilistic Analysis Check dashBOARD

Development of a pharmacoeconomic registry: an example using hormonal contraceptives

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