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Medical Decision Making
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Decision-Analytic Modeling to Evaluate Benefits and Harms of Medical Tests: Uses and Limitations

Thomas A. Trikalinos, MD

Tufts Evidence-based Practice Center and Center for Clinical Evidence Synthesis, Tufts Medical Center, Boston, Massachusetts, ttrikalin{at}mac.com

Uwe Siebert, MD, MPH, MSc, ScD

Department of Public Health, Medical Decision Making and Health Technology Assessment UMIT-University for Health Sciences, Medical Informatics and Technology, Hall I. T., Austria, Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical, School and Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts

Joseph Lau, MD

Tufts Evidence-based Practice Center and Center for Clinical Evidence Synthesis, Tufts Medical Center, Boston, Massachusetts

The clinical utility of medical tests is measured by whether the information they provide affects patient-relevant outcomes. To a large extent, effects of medical tests are indirect in nature. In principle, a test result affects patient outcomes mainly by influencing treatment choices. This indirectness in the link between testing and its downstream effects poses practical challenges to comparing alternate test-and-treat strategies in clinical trials. Keeping in mind the broader audience of researchers who perform comparative effectiveness reviews and technology assessments, the authors summarize the rationale for and pitfalls of decision modeling in the comparative evaluation of medical tests by virtue of specific examples. Modeling facilitates the interpretation of test performance measures by connecting the link between testing and patient outcomes, accounting for uncertainties and explicating assumptions, and allowing the systematic study of tradeoffs and uncertainty. The authors discuss challenges encountered when modeling test-and-treat strategies, including but not limited to scarcity of data on important parameters, transferring estimates of test performance across studies, choosing modeling outcomes, and obtaining summary estimates for test performance data.

Key Words: Bayesian meta-analysis • comparative effectiveness • multiparameter evidence synthesis • systematic reviews • meta-analysis • evidence synthesis • decision analysis.

This version was published on September 1, 2009

Medical Decision Making, Vol. 29, No. 5, E22-E29 (2009)
DOI: 10.1177/0272989X09345022
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Med Decis Making, September 1, 2009; 29(5): 634 - 635.
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