OBJECTIVES: Compare three commonly used methods to combine the impacts of multiple health conditions on SF-6D health utility scores. STUDY DESIGN AND SETTING: We used data from the 1998-2004 Medicare Health Outcomes Survey to compare three commonly suggested models of multiple health conditions' impacts on health-related quality of life: additive, minimum, and multiplicative. We modeled SF-6D scores using information about 15 health conditions, both unadjusted and adjusted for age, sex, education, and income. Model performance was assessed using mean squared error, mean predictive error by number of health conditions, and mean predictive error for groups with specific combinations of health conditions. RESULTS: Ninety-five thousand one hundred ninety-five observations were used for model estimation, and 94,794 observations were used for model testing. The adjusted models always had better performance than the unadjusted models. The multiplicative model showed smaller mean predictive error than the other models in both those younger than 65 years and those 65 years and older. Mean predictive error for the multiplicative model was generally within the minimally important difference of the SF-6D. CONCLUSION: All tested models are imperfect in these Medicare data, but the multiplicative model performed best.
OBJECTIVES: Compare three commonly used methods to combine the impacts of multiple health conditions on SF-6D health utility scores. STUDY DESIGN AND SETTING: We used data from the 1998-2004 Medicare Health Outcomes Survey to compare three commonly suggested models of multiple health conditions' impacts on health-related quality of life: additive, minimum, and multiplicative. We modeled SF-6D scores using information about 15 health conditions, both unadjusted and adjusted for age, sex, education, and income. Model performance was assessed using mean squared error, mean predictive error by number of health conditions, and mean predictive error for groups with specific combinations of health conditions. RESULTS: Ninety-five thousand one hundred ninety-five observations were used for model estimation, and 94,794 observations were used for model testing. The adjusted models always had better performance than the unadjusted models. The multiplicative model showed smaller mean predictive error than the other models in both those younger than 65 years and those 65 years and older. Mean predictive error for the multiplicative model was generally within the minimally important difference of the SF-6D. CONCLUSION: All tested models are imperfect in these Medicare data, but the multiplicative model performed best.
Authors: Susanne Schmitz; Tatjana T Makovski; Roisin Adams; Marjan van den Akker; Saverio Stranges; Maurice P Zeegers Journal: Pharmacoeconomics Date: 2020-01 Impact factor: 4.981
Authors: Neda Laiteerapong; Jennifer M Cooper; M Reza Skandari; Philip M Clarke; Aaron N Winn; Rochelle N Naylor; Elbert S Huang Journal: Ann Intern Med Date: 2017-12-12 Impact factor: 25.391
Authors: Nicole Mittmann; Natasha K Stout; Pablo Lee; Anna N A Tosteson; Amy Trentham-Dietz; Oguzhan Alagoz; Martin J Yaffe Journal: Health Rep Date: 2015-12 Impact factor: 4.796
Authors: Henk B M Hilderink; Marjanne H D Plasmans; Bianca E P Snijders; Hendriek C Boshuizen; M J J C René Poos; Coen H van Gool Journal: Arch Public Health Date: 2016-08-22