Chapter 10: deciding whether to complement a systematic review of medical tests with decision modeling.
Limited by what is reported in the literature, most systematic reviews of medical tests focus on "test accuracy" (or better, test performance), rather than on the impact of testing on patient outcomes. The link between testing, test results and patient outcomes is typically complex: even when testing has high accuracy, there is no guarantee that physicians will act according to test results, that patients will follow their orders, or that the intervention will yield a beneficial endpoint. Therefore, test performance is typically not sufficient for assessing the usefulness of medical tests. Modeling (in the form of decision or economic analysis) is a natural framework for linking test performance data to clinical outcomes. We propose that (some) modeling should be considered to facilitate the interpretation of summary test performance measures by connecting testing and patient outcomes. We discuss a simple algorithm for helping systematic reviewers think through this possibility, and illustrate it by means of an example.
Center for Evidence-based Medicine, and Department of Health Services Policy and Practice, Brown University, Providence, RI 02912, USA. thomas_trikalinos@brown.edu
Thomas A
TA
Trikalinos
Shalini
S
Kulasingam
William F
WF
Lawrence
D006801:Humans; D000465:Algorithms; D003661:Decision Support Techniques; D019937:Diagnostic Techniques and Procedures / Q000592:standards; D019317:Evidence-Based Medicine / Q000379:methods / Q000592:standards; D017408:Guidelines as Topic; D012680:Sensitivity and Specificity; D000078202:Systematic Reviews as Topic