title: Predicting utility for joint health states: a general framework and a new nonparametric estimator.
abstract: Measuring utility is important in clinical decision making and cost-effectiveness analysis because utilities are often used to compute quality-adjusted life expectancy, a metric used in measuring the effectiveness of health care programs and medical interventions. Predicting utility for joint health states has become an increasingly valuable research topic because of the aging of the population and the increasing prevalence of comorbidities. Although multiplicative, minimum, and additive estimators are commonly used in practice, research has shown that they are all biased. In this study, the authors propose a general framework for predicting utility for joint health states. This framework includes these 3 nonparametric estimators as special cases. A new simple nonparametric estimator, the adjusted decrement estimator, [U(ij) = U(min) - U(min) (1 - U(i) )(1 - U(j) )], is introduced under the proposed framework. When applied to 2 independent data sources, the new nonparametric estimator not only generated unbiased prediction of utilities for joint health states but also had the least root mean squared error and highest concordance when compared with other nonparametric and parametric estimators. Further research and validation of this new estimator are needed.
authors.affiliation: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
authors.lastname: Bo Hu
authors.forename: Alex Z
authors.initials: AZ
authors.lastname: Fu
mesh_terms: D000328:Adult; D015897:Comorbidity; D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data; D005260:Female; D005544:Forecasting; D006304:Health Status; D006801:Humans; D007596:Joints / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D018709:Statistics, Nonparametric