BACKGROUND: The SF-36 and the Quality of Well-being index (QWB) both quantify health status, yet have very different methodologic etiologies. The authors sought to develop an empirical equation allowing prediction of the QWB from the SF-36. DATA: They used empirical observations of SF-36 profiles and QWB scores collected in interviews of 1,430 persons during the Beaver Dam Health Outcomes Study, a community-based population study of health status, and 57 persons from a renal dialysis clinic. METHOD: The eight scales of the SF-36, their squares, and all pairwise cross-products, were used as candidate variables in stepwise and best-subsets regressions to predict QWB scores using 1,356 interviews reported in a previous paper. The resulting equation was cross-validated on the remaining 74 cases and using the renal dialysis patients. RESULTS: A six-variable regression equation drawing on five of the SF-36 components predicted 56.9% of the observed QWB variance. The equation achieved an R2 of 49.5% on cross-validation using Beaver Dam participants and an R2 of 58.7% with the renal dialysis patients. An approximation for computing confidence intervals for predicted QWB mean scores is given. CONCLUSION: SF-36 data may be used to predict mean QWB scores for groups of patients, and thus may be useful to modelers who are secondary users of health status profile data. The equation may also be used to provide an overall health utility summary score to represent SF-36 profile data so long as the profiles are not severely limited by floor or ceiling effects of the SF-36 scales. The results of this study provide a quantitative link between two important measures of health status.
BACKGROUND: The SF-36 and the Quality of Well-being index (QWB) both quantify health status, yet have very different methodologic etiologies. The authors sought to develop an empirical equation allowing prediction of the QWB from the SF-36. DATA: They used empirical observations of SF-36 profiles and QWB scores collected in interviews of 1,430 persons during the Beaver Dam Health Outcomes Study, a community-based population study of health status, and 57 persons from a renal dialysis clinic. METHOD: The eight scales of the SF-36, their squares, and all pairwise cross-products, were used as candidate variables in stepwise and best-subsets regressions to predict QWB scores using 1,356 interviews reported in a previous paper. The resulting equation was cross-validated on the remaining 74 cases and using the renal dialysis patients. RESULTS: A six-variable regression equation drawing on five of the SF-36 components predicted 56.9% of the observed QWB variance. The equation achieved an R2 of 49.5% on cross-validation using Beaver Dam participants and an R2 of 58.7% with the renal dialysis patients. An approximation for computing confidence intervals for predicted QWB mean scores is given. CONCLUSION: SF-36 data may be used to predict mean QWB scores for groups of patients, and thus may be useful to modelers who are secondary users of health status profile data. The equation may also be used to provide an overall health utility summary score to represent SF-36 profile data so long as the profiles are not severely limited by floor or ceiling effects of the SF-36 scales. The results of this study provide a quantitative link between two important measures of health status.
Authors: Angela Marinilli Pinto; Leslee L Subak; Sanae Nakagawa; Eric Vittinghoff; Rena R Wing; John W Kusek; William H Herman; Delia Smith West; Miriam Kuppermann Journal: Qual Life Res Date: 2011-12-10 Impact factor: 4.147
Authors: Ted Miller; Soma Bhattacharya; William Zamula; Dennis Lezotte; Karen Kowalske; David Herndon; James Fauerbach; Loren Engrav Journal: Qual Life Res Date: 2012-12-08 Impact factor: 4.147