Quantitative Methods in Health Care Decisions
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 19265
Special Issue Editors
Interests: Bayesian statistics
Interests: health economics; bayesian methods in health economics; cost-effectiveness analysis; meta-analysis and equity in healthcare services
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Health economics is defined as the application of the theories, concepts, and techniques of economics to any issue related to health. There has been growing interest in this field, because health concerns represent a very important part of public government spending. The growing interest in recent years can be explained partly by reduced economic growth, deficits in public budgets, and increased unemployment rates and partly by the introduction of more expensive healthcare technologies. Moreover, the increasing number of treatments that can potentially be used to improve health makes the selection of cost-effective treatments even more necessary. This has led to increased attention to economic research questions in the health sector.
In this context, optimal decision making has become even more important, but decisions in healthcare systems are very complex due to their complicated design and their nonlinear, dynamic, and unpredictable nature. These characteristics require the inclusion of many elements to the support decision process, and advanced decision-making tools are necessary. Parametric and non-parametric modeling methods, network meta-analysis, matching methods in impact evaluation, or the use of Bayesian methods in healthcare and medicine, due mainly to the popularity of MCMC techniques, are just some examples of the progress in this area of quantitative methods in recent years.
This Special Issue will serve as an outlet for research papers using advanced computational and/or statistical methods for health economics, in general, as well as other particular topics such as cost-effectiveness, health policy evaluation, etc. This Special Issue will become a valuable resource for well-founded theoretical and applied data-driven research. Submissions should contain a significant computational or statistical methodological component for data analytics. In particular, this Special Issue welcomes contributions focusing on statistics that address problems involving large and/or complex data. Emphasis will be given to comprehensive and reproducible research, including data-driven methodology, algorithms, etc. Potential topics include, but are not limited to, the following:
- Bayesian methods in health economics;
- Network meta-analysis;
- Machine learning for health economics researchers;
- Parametric and non-parametric modeling of health data;
- Computational methods for health data.
Prof. Dr. Francisco-José Vázquez-Polo
Dr. Miguel Angel Negrín Hernández
Guest Editors
Manuscript Submission Information
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Keywords
- Health economics
- Quantitative methods
- Cost-effectiveness
- Meta-analysis
- Healthcare decisions
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