Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | S76-S82 |
| Journal | Journal of general internal medicine |
| Volume | 27 |
| Issue number | SUPPL.1 |
| DOIs | |
| State | Published - Jun 2012 |
Bibliographical note
Funding Information:Acknowledgment: This manuscript is based on work funded by the Agency for Healthcare Research and Quality (AHRQ). Authors TT and SK are members of AHRQ-funded Evidence-based Practice Centers, and author WL is an AHRQ employee. The opinions expressed are those of the authors and do not reflect the official position of AHRQ or the U.S. Department of Health and Human Services.
Keywords
- decision modeling
- medical tests
- systematic review
- test accuracy
- test performance