Med Decis Making - Predicting utility for joint health states: a general framework and a new nonparametric estimator.

Tópicos

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Resumo

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.

Resumo Limpo

measur util import clinic decis make costeffect analysi util often use comput qualityadjust life expect metric use measur effect health care program medic intervent predict util joint health state becom increas valuabl research topic age popul increas preval comorbid although multipl minimum addit estim common use practic research shown bias studi author propos general framework predict util joint health state framework includ nonparametr estim special case new simpl nonparametr estim adjust decrement estim uij umin umin ui uj introduc propos framework appli independ data sourc new nonparametr estim generat unbias predict util joint health state also least root mean squar error highest concord compar nonparametr parametr estim research valid new estim need

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