Methods to explain the clinical significance of health status measures

Gordon H. Guyatt*, David Osoba, Albert W. Wu, Kathleen W. Wyrwich, Geoffrey R. Norman, Neil Aaronson, Ivan Barofsky, Rick Berzon, Amy Bonomi, Monika Bullinger, Joseph C. Cappelleri, David Cella, Diane Fairclough, Carol Estwing Ferrans, Marlene Frost, Ron D. Hays, Patrick D. Marquis, Carol M. Moinpour, Tim Moynihan, Donald PatrickDennis Revicki, Teresa Rummans, Charles Scott, Jeff A. Sloan, Mirjam Sprangers, Tara Symonds, Claudette Varricchio, Gilbert Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

958 Scopus citations

Abstract

One can classify ways to establish the interpretability of quality-of-life measures as anchor based or distribution based. Anchor-based measures require an independent standard or anchor that is itself interpretable and at least moderately correlated with the instrument being explored. One can further classify anchor-based approaches into population-focused and individual-focused measures. Population-focused approaches are analogous to construct validation and rely on multiple anchors that frame an individual's response in terms of the entire population (eg, a group of patients with a score of 40 has a mortality of 20%). Anchors for population-based approaches include status on a single item, diagnosis, symptoms, disease severity, and response to treatment. Individual-focused approaches are analogous to criterion validation. These methods, which rely on a single anchor and establish a minimum important difference in change in score, require 2 steps. The first step establishes the smallest change in score that patients consider, on average, to be important (the minimum important difference). The second step estimates the proportion of patients who have achieved that minimum important difference. Anchors for the individual-focused approach include global ratings of change within patients and global ratings of differences between patients. Distribution-based methods rely on expressing an effect in terms of the underlying distribution of results. Investigators may express effects in terms of between-person standard deviation units, within-person standard deviation units, and the standard error of measurement. No single approach to interpretability is perfect. Use of multiple strategies is likely to enhance the interpretability of any particular instrument.

Original languageEnglish (US)
Article number61793
Pages (from-to)371-383
Number of pages13
JournalMayo Clinic Proceedings
Volume77
Issue number4
DOIs
StatePublished - 2002

ASJC Scopus subject areas

  • Medicine(all)

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