Mapping the PHQ-8 to EQ-5D, HUI3 and SF6D in Patients with Depression

Abstract

There is limited evidence of mapping the clinical instrument to a generic preference-based instrument in an Asian patient population. The current study aims to map the eight-item Patient Health Questionnaire depression scale (PHQ) onto the EuroQol five-dimensional (EQ-5D), the Health Utilities Index Mark 3 (HUI3) and the Short Form-6D SF-36 to inform future in cost-utility analyses for treatment in depression sample. A total of 249 participants who have completed PHQ-8, EQ5D, SF6D and HUI-3 questionnaires were included in the analyses. A beta regression mixture model was used to map the utility scores. The results were compared against two common regression methods including Ordinary Least Square (OLS) and Tobit regression models. The mean age of the sample was 36.2 years (SD=11.1). The mean EQ-5D-3L, EQ-5D-5L, HUI3 and SF-6D utility scores were 0.615, 0.709, 0.461 and 0.607, respectively. The EQ-5D-3L, EQ-5D-5L and SF-6D utility scores were best predicted by the beta mixture regression model consisting of PHQ total sores, PHQ-squared, and covariates including age and gender. The HUI3 was best predicted by the OLS model. The current study provides important evidence to clinicians and researchers about the mapping algorithms that can be used in economics evaluation among patients with depression.

Mapping the Memorial Anxiety Scale for Prostate Cancer to the SF-6D

“…Beta regression is a flexible approach for modeling and predicting variables between 0 and 1, while mixture modeling allows for specification of multimodal distributions (commonly seen with utilities) as combinations of simpler distributions (or components) [28,29]. We used beta regression mixture models because of their superior performance over other regression-based or machine learning techniques (including ordinary least squares, generalized ordered probit/logit models, generalized linear models, fractional regression, robust MM estimator and adjusted limited dependent variable mixture models) when generating utility crosswalks from PRO measures [30][31][32][33][34][35]. We assessed crosswalk performance using objective and subjective measures of prediction accuracy (i.e., mean absolute errors [MAE], root mean-squared error [RMSE], Akaike information criterion [AIC], Bayesian information criterion [BIC], the proportion of predictions that lie within ± 5 and ± 10% of observed SF-6D and correlations between observed and predicted SF-6D) [36].…”