Dataset Information

Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV.

ABSTRACT: Objectives: The Medical Outcomes Study HIV Health Survey (MOS-HIV) is frequently used in HIV clinical trials; however, scores generated from the MOS-HIV are not suited for cost-effectiveness analyses as they do not assign utility values to health states. Our objective was to estimate and externally validate several mapping algorithms to predict Health Utilities Index Mark 3 (HUI3) and EQ-5D-3L utility values from the MOS-HIV. Methods: We developed and validated mapping algorithms using data from two HIV clinical trials. Data from the first trial (n = 367) formed the estimation data set for the HUI3 (4,610 observations) and EQ-5D-3L (4,662 observations) mapping algorithms; data from the second trial (n = 168) formed the HUI3 (1,135 observations) and EQ-5D-3L (1,152 observations) external validation data set. We compared ordinary least squares (OLS) models of increasing complexity with the more flexible two-part, beta regression, and finite mixture models. We assessed model performance using mean absolute error (MAE) and mean squared error (MSE). Results: The OLS model that used MOS-HIV dimension scores along with squared terms gave the best HUI3 predictions (mean observed 0.84; mean predicted 0.80; MAE 0.0961); the finite mixture model gave the best EQ-5D-3L predictions (mean observed 0.90; mean predicted 0.88; MAE 0.0567). All models produced higher prediction errors at the lower end of the HUI3 and EQ-5D-3L score ranges (<0.40). Conclusions: The proposed mapping algorithms can be used to predict HUI3 and EQ-5D-3L utility values from the MOS-HIV, although greater error may pose a problem in samples where a substantial proportion of patients are in poor health. These algorithms may be useful for estimating utility values from the MOS-HIV for cost-effectiveness studies when HUI3 or EQ-5D-3L data are not available.

SUBMITTER: Joyce VR

PROVIDER: S-EPMC6125043 | biostudies-literature | 2017 Jul-Dec

REPOSITORIES: biostudies-literature

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Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV.

Joyce Vilija R VR Sun Huiying H Barnett Paul G PG Bansback Nick N Griffin Susan C SC Bayoumi Ahmed M AM Anis Aslam H AH Sculpher Mark M Cameron William W Brown Sheldon T ST Holodniy Mark M Owens Douglas K DK

MDM policy & practice 20170703 2

<b>Objectives:</b> The Medical Outcomes Study HIV Health Survey (MOS-HIV) is frequently used in HIV clinical trials; however, scores generated from the MOS-HIV are not suited for cost-effectiveness analyses as they do not assign utility values to health states. Our objective was to estimate and externally validate several mapping algorithms to predict Health Utilities Index Mark 3 (HUI3) and EQ-5D-3L utility values from the MOS-HIV. <b>Methods:</b> We developed and validated mapping algorithms u ...[more]

PMID: 30288427

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Project description:BackgroundThe types of outcomes measured collected in clinical studies and those required for cost-effectiveness analysis often differ. Decision makers routinely use quality adjusted life years (QALYs) to compare the benefits and costs of treatments across different diseases and treatments using a common metric. QALYs can be calculated using preference-based measures (PBMs) such as EQ-5D-3L, but clinical studies often focus on objective clinician or laboratory measured outcomes and non-preference-based patient outcomes, such as QLQ-C30. We model the relationship between the generic, preference-based EQ-5D-3L and the cancer specific quality of life questionnaire, QLQ-C30 in patients with breast cancer. This will result in a mapping that allows users to convert QLQ-C30 scores into EQ-5D-3L scores for the purposes of cost-effectiveness analysis or economic evaluation.MethodsWe use data from a randomized trial of 602 patients with HER2-positive advanced breast cancer provided 3766 EQ-5D-3L observations. Direct mapping using adjusted, limited dependent variable mixture models (ALDVMM) is compared to a random effects linear regression and indirect mapping using seemingly unrelated ordered probit models. EQ-5D-3L was estimated as a function of the summary scales of the QLQ-C30 and other patient characteristics.ResultsA four component mixture model outperformed other models in terms of summary fit statistics. A close fit to the observed data was observed across the range of disease severity. Simulated data from the model closely aligned to the original data and showed that mapping did not significantly underestimate uncertainty. In the simulated data, 22.15% were equal to 1 compared to 21.93% in the original data. Variance was 0.0628 in the simulated data versus 0.0693 in the original data. The preferred mapping is provided in Excel and Stata files for the ease of users.ConclusionA four component adjusted mixture model provides reliable, non-biased estimates of EQ-5D-3L from the QLQ-C30, to link clinical studies to economic evaluation of health technologies for breast cancer. This work adds to a growing body of literature demonstrating the appropriateness of mixture model based approaches in mapping.

Project description:Background The adult versions EQ-5D-3L and EQ-5D-5L have been extensive compared. This is not the case for the EQ-5D youth versions. The study aim was to compare the measurement properties and responsiveness of EQ-5D-Y-3L and EQ-5D-Y-5L in paediatric patients. Methods A sample of patients 8–16 years old with different diseases and a wide range of disease severity was asked to complete EQ-5D-Y-3L, EQ-5D-Y-5L, PedsQL Generic Core Scale, and selected, appropriate disease-specific instruments, three times. EQ-5D-Y-3L and EQ-5D-Y-5L were compared in terms of: feasibility, (re-)distribution properties, discriminatory power, convergent validity, test–retest reliability, and responsiveness. Results 286 participating patients suffered from one of the following diseases: major beta-thalassemia, haemophilia, acute lymphoblastic leukaemia, acute illness. Missing responses were comparable between versions of the EQ-5D-Y, suggesting comparable feasibility. The number of patients in the best health state (level profile 11111) was equal in both EQ-5D-Y versions. The projection of EQ-5D-Y-3L scores onto EQ-5D-Y-5L for all dimensions showed that the two additional levels in EQ-5D-Y-5L slightly improved the accuracy of patients in reporting their problems, especially if severe. Convergent validity with PedsQL and disease-specific measures showed that the two EQ-5D-Y versions performed about equally. Test–retest reliability (EQ-5D-Y-3L 0.78 vs EQ-5D-Y-5L 0.84), and sensitivity for detecting health changes, were both better in EQ-5D-Y-5L. Conclusions Extending the number of levels did not give clear superiority to EQ-5D-Y-5L over EQ-5D-Y-3L based on the criteria assessed in this study. However, increasing the number of levels benefitted EQ-5D-Y performance in the measurement of moderate to severe problems and especially in longitudinal study designs. Supplementary Information The online version contains supplementary material available at 10.1186/s12955-021-01889-4.

Dataset Information

Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV.

Publications

Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV.

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