Decision Analysis and Modeling

Systematic reviewers are often asked to critically appraise existing decision models as part of their review. More currently, systematic reviews also produce, or collaborate with groups that produce, new decision models, micro- simulation models, and cost-effectiveness analyses to extend their findings and support decision-makers in health care.

Decision modeling can be used to predict how different kinds of screening programs or interventions might improve long-term health outcomes, improve program efficiency, or reduce disparities. They can also compare different screening intervals and project longer-term health impacts beyond the timeframe covered by trials.

The KPRA EPC has a well-established group of health economists and modelers who have worked collaboratively with systematic reviewers and other researchers. For example, our researchers have been involved with the US Preventive Services Task Force’s experience using decision modeling to weigh the benefits and harms of screening for colorectal cancer and screening for cervical cancer, and the Task Force on Community Preventive Services’ systematic assessments of the literature on cost and cost-effectiveness for decision-makers to use alongside their evidence-based recommendation on effectiveness.

Our EPC has extensive experience in decision modeling to assess the value of a wide range of preventive services, including micro-simulation models of breast, cervical and colorectal cancer screening, cardiovascular disease prevention, tobacco use, obesity screening, and STD/HIV screening.

KPRA EPC researchers also have extensive experience developing methods around decision modeling, including abstraction tools for using economic results in evidence reports, methods for standardizing cost-effectiveness data for the Committee on Clinical Preventive Services, methods for obtaining consistent estimates of cost-effectiveness to allow valid comparisons of the value of evidence-based services recommended by the USPSTF, and methods for and validation of microsimulation modeling.

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