Transforming Self-Rated Health and the SF-36 Scales to Include Death and Improve Interpretability

Datos Bibliográficos

ID9101464
AutoresPaula Diehr (University of Washington, autor de correspondencia), Donald L Patrick (0000-0003-0756-0492, University of Washington), John Spertus, John A Spertus (0000-0002-2839-2611, University of Missouri–Kansas City), Catarina I Kiefe (0000-0001-8719-6963, University of Washington, autor de correspondencia), Mary B McDonell (VA Puget Sound Health Care System), Mary McDonell, Stephan D Fihn (University of Washington)
Año2001
Volumen39
Número7
Páginas670-680
Fecha de publicación2001-07-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-200107000-00004
PMID11458132
OpenAlexW1974187277
IdiomaEN
Citas recibidas14
Referencias citadas16

BACKGROUND: Most measures of health-related quality of life are undefined for people who die. Longitudinal analyses are often limited to a healthier cohort (survivors) that cannot be identified prospectively, and that may have had little change in health. OBJECTIVE: To develop and evaluate methods to transform a single self-rated health item (excellent to poor; EVGGFP) and the physical component score of the SF-36 (PCS) to new variables that include a defensible value for death. METHODS: Using longitudinal data from two large studies of older adults, health variables were transformed to the probability of being healthy in the future, conditional on the current observed value; death then has the value of 0. For EVGGFP, the new transformations were compared with some that were published earlier, based on different data. For the PCS, how well three different transformations, based on different definitions of being healthy, discriminated among groups of patients, and detected change in time were assessed. RESULTS: The new transformation for EVGGFP was similar to that published previously. Coding the 5 categories as 95, 90, 80, 30, and 15, and coding dead as 0 is recommended. The three transformations of the PCS detected group differences and change at least as well as the standard PCS. CONCLUSION: These easily interpretable transformed variables permit keeping persons who die in the analyses. Using the transformed variables for longitudinal analyses of health when deaths occur, either for secondary or primary analysis, is recommended. This approach can be applied to other measures of health

Coding (social sciences) · Cohort · Data mining · Interpretability · Longitudinal data · Longitudinal study · Quality of life (healthcare) · Statistics · Artificial Intelligence · Chronic Disease Management Strategies · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Internal Medicine · Mathematics · Medicine · Palliative Care and End-of-Life Issues · Psychology · Gerontology