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Transforming the SF-36 to Account for Death in Longitudinal Studies With Three-Year Follow-Up

Bibliographic Data

ID9103950
AuthorsSteven Bowe, Steven J Bowe (0000-0003-3813-842X, University of Newcastle Australia, corresponding author), Anne Young (0000-0002-6424-7155, University of Newcastle Australia), Anne F Young, David Sibbritt (0000-0003-3561-9447, University of Newcastle Australia, corresponding author), Hiroyuki Furuya (0000-0003-0222-4174, Tokai University)
Year2006
Volume44
Issue10
Pages956-959
Publication date2006-10-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/01.mlr.0000228022.79359.95
PMID17001268
OpenAlexW2025399120
LanguageEN
Citations received1
References cited14

BACKGROUND: Analyses of longitudinal health-related quality of life data often exclude participants who die, which limits the generalizability of the results. Methods to incorporate death as a valid score in the Medical Outcomes Study Short-Form (SF-36) have been suggested but need to be evaluated in other populations. OBJECTIVES: We sought to apply a method of transforming the SF-36 Physical Component Score (PCS) to include death. A transformation to estimate the probability of being "healthy" in 3 years, based on the current PCS value, will be developed and validated. SUBJECTS: Women in the Australian Longitudinal Study on Women's Health (ALSWH), ages 70-75 years at Survey 1 in 1996 (n = 12,432), were followed-up at 3 yearly intervals for 6 years. RESULTS: The transformation derived from the ALSWH data provides evidence that the methodology for transforming the PCS to account for deaths is sound. The 3-year equation provided good estimates of the probability of being healthy in 3 years and the method allowed deaths to be included in an analysis of changes in health over time. CONCLUSIONS: For longitudinal studies involving the SF-36 in which subjects have died, we support the recommendation that both the PCS and its transformed value which includes deaths should be analyzed to examine the influence of deaths on the study conclusions. Using study data to derive empirical parameters for the transformations may be appropriate for studies with follow-up intervals of other lengths

MEDLINE · Political science · Chronic Disease Management Strategies · Health disparities and outcomes · Health Systems, Economic Evaluations, Quality of Life · Medicine · Psychology · Gerontology

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Unique citing works1
Citations per year0,05
Citation span2007 - 2007 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 1

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