Kuntz Leads Addition of Computer Modeling for Cost-Effectiveness Analysis Guidelines

									Charlie Plain |
																			September 28, 2016
					
Professor Karen Kuntz
Professor Karen Kuntz

A paper published in JAMA offers new guidelines in health care cost-effectiveness analysis (CEA), replacing recommendations published in 1996. Professor Karen Kuntz was a member of the panel charged with drafting the new guidelines and led the writing of an additional section on developing computer models for conducting CEAs.

In the two decades since the first guidelines were written, computer models have become a common tool used in completing a CEA.

“Enough time has passed, and there have been a lot of changes in the cost-effectiveness analysis field — like the increase in the use of decision-analytic models — so we knew an update was needed,” says Kuntz.

A CEA is a decision-making tool in which the costs and effects of tests, therapies, and prevention techniques are calculated. The results of CEAs are used by governments in making health care coverage decisions. Guidelines help ensure that the results from any one analysis can be compared to another.

The computer models used in CEAs, such as decision-analytic models, can take evidence from multiple sources and use it to extend results to different populations or to make projections beyond the time horizon of collected data.

Kuntz sought to provide recommendations for modeling that produce relevant, reliable, and useful projections.

“We emphasized transparency and the importance of users being clear in describing the assumptions they make in creating a model’s structure,” says Kuntz. “We recommended incorporating all the available data, and being clear about why you did or did not include certain sources. We also discussed the importance of adopting a lifetime horizon to capture all of the costs and effectiveness that may be relevant to decision makers.”

Kuntz said the new CEA guidelines will hopefully be used as the method of analysis by researchers and advance best-practice discussions within the field.

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