Mapping for the EQ-5D-5L for Use in Cost-Utility Analysis

• Author / Creator

  Wen, Jiabi

• Cost-utility analysis (CUA) assesses the cost-effectiveness of health technologies by comparing their costs and health outcomes. The utility is incorporated in the health outcome measures of CUA, and the EQ-5D-5L is one of the most common instruments to estimate utility values. When utility values are not available, mapping from non-preference-based instruments to a preference-based instrument is a popular technique. When CUAs use different preference-based measures, mapping between these measures can transfer the utility values and allow for better comparison across CUAs. However, there are many concerns regarding studies reporting mapped utility values, such as extrapolation issues and the uncertainty of this methodology. The quality of mapping studies has become an important criterion when using them in economic evaluations. The first study of my thesis assessed the reporting quality of mapping studies onto the EQ-5D-5L, especially their completeness of information for CUA applications. The second study developed a novel mapping algorithm from the Edmonton Symptom Assessment System Revised: Renal (ESAS-r: Renal) to the EQ-5D-5L among patients with end-stage renal disease (ESRD).

  The first objective of my systematic review was to identify new mapping studies onto the EQ-5D-5L by updating a previous systematic search made by the Health Economics Research Centre (HERC). The second objective was to assess all the EQ-5D-5L mapping studies on their reporting quality, especially the completeness of information for CUA, with the use of two reporting quality checklists. The third objective was to explore whether using reporting quality checklists was associated with improved reporting quality. The review identified 14 new studies since 2018 which were not included in the HERC database. In the assessment of all 39 published studies (including 25 from the HERC database), the overall reporting quality was mostly good. In several areas I identified problems that would require improvements including 1) estimation of predicted utilities, 2) reporting variances, covariances, and error terms, 3) final model calculation examples, 4) parameter uncertainty, and 5) individual uncertainty. A preliminary comparison showed that the checklists could help to improve the reporting quality of the studies.

  The second study of this thesis mapped the ESAS-r: Renal to the EQ-5D-5L in patients with ESRD using data from the Evaluation of Routinely Measured Patient-reported Outcomes in Hemodialysis Care (EMPATHY) trial, a multi-centre clustered randomized-controlled trial of routine measurement of patient-reported outcomes in hemodialysis units in Northern Alberta. Several models were explored in the mapping analysis using data from one study arm, including linear models, censored dependent variable models, mixture models and response mapping. Internal validation was conducted to evaluate the predictive power of the models, and the validation sample was from another arm of the EMPATHY trial. Statistical fit and predictive power were measured by mean absolute error (MAE) and mean squared error (MSE), which, along with theoretical backgrounds, were the selection criteria for the best model. The final sample size for model estimation was 506, after excluding missing records (missing rate: 57.6%). All models produced relatively similar statistical fit and predictive power (Estimation: MAE: 0.056 - 0.120, MSE: 0.007 - 0.028; Validation: MAE: 0.136 - 0.155, MSE: 0.032 - 0.046). All models showed great prediction properties for relatively healthy health states, but poor prediction properties for worse health states. With the consideration of all selection criteria, the generalized estimating equations (Estimation: MAE: 0.120, MSE: 0.027; Validation: MAE:0.140, MSE:0.034 ) and generalized linear models (Estimation: MAE: 0.116, MSE: 0.028; Validation: MAE: 0.136, MSE: 0.034) on selected ESAS-r: Renal items were considered the best models. Since the models have not been externally validated, they should be applied in populations with similar patient characteristics as our study sample.

  Overall, mapping is a feasible and useful technique to estimate the utility values for conducting CUA. The issues identified in current mapping studies could inform further mapping studies on how to improve reporting quality, especially ensuring the completeness of information for employing mapping algorithms in CUA. The empirical mapping study on ESAS-r: Renal provided mapping algorithms which could be used to predict utility values for patients with ESRD when only ESAS-r: Renal is available.

• Subjects / Keywords

  • Mapping
  • EQ-5D-5L
  • ESAS-r: Renal
  • Cost-Utility Analysis

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-yyv4-bp08

• License

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