Developing the Breast Utility Instrument to Measure Health-Related Quality-of-Life Preferences in Patients with Breast Cancer: Selecting the Item for Each Dimension

A scoping review to create a framework for the steps in developing condition-specific preference-based instruments de novo or from an existing non-preference-based instrument: use of item response theory or Rasch analysis

BACKGROUND

There is no widely accepted framework to guide the development of condition-specific preference-based instruments (CSPBIs) that includes both de novo and from existing non-preference-based instruments. The purpose of this study was to address this gap by reviewing the published literature on CSPBIs, with particular attention to the application of item response theory (IRT) and Rasch analysis in their development.

METHODS

A scoping review of the literature covering the concepts of all phases of CSPBI development and evaluation was performed from MEDLINE, Embase, PsychInfo, CINAHL, and the Cochrane Library, from inception to December 30, 2022.

RESULTS

The titles and abstracts of 1,967 unique references were reviewed. After retrieving and reviewing 154 full-text articles, data were extracted from 109 articles, representing 41 CSPBIs covering 21 diseases or conditions. The development of CSPBIs was conceptualized as a 15-step framework, covering four phases: 1) develop initial questionnaire items (when no suitable non-preference-based instrument exists), 2) establish the dimensional structure, 3) reduce items per dimension, 4) value and model health state utilities. Thirty-nine instruments used a type of Rasch model and two instruments used IRT models in phase 3.

CONCLUSION

We present an expanded framework that outlines the development of CSPBIs, both from existing non-preference-based instruments and de novo when no suitable non-preference-based instrument exists, using IRT and Rasch analysis. For items that fit the Rasch model, developers selected one item per dimension and explored item response level reduction. This framework will guide researchers who are developing or assessing CSPBIs.

Creating a Multiply Imputed Value Set for the EQ-5D-5L in Canada: State-Level Misspecification Terms Are Needed to Characterize Parameter Uncertainty Correctly

BACKGROUND

Parameter uncertainty in EQ-5D-5L value sets often exceeds the instrument's minimum important difference, yet this is routinely ignored. Multiple imputation (MI) accounts for parameter uncertainty in the value set; however, no valuation study has implemented this methodology. Our objective was to create a Canadian MI value set for the EQ-5D-5L, thus enabling users to account for parameter uncertainty in the value set.

METHODS

Using the Canadian EQ-5D-5L valuation study (N = 1,073), we first refit the original model followed by models with state-level misspecification. Models were compared based on the adequacy of 95% credible interval (CrI) coverage for out-of-sample predictions. Using the best-fitting model, we took 100 draws from the posterior distribution to create 100 imputed value sets. We examined how much the standard error of the estimated mean health utilities increased after accounting for parameter uncertainty in the value set by using the MI and original value sets to score 2 data sets: 1) a sample of 1,208 individuals from the Canadian general public and 2) a sample of 401 women with breast cancer.

RESULTS

The selected model with state-level misspecification outperformed the original model (95% CrI coverage: 94.2% v. 11.6%). We observed wider standard errors for the estimated mean utilities on using the MI value set for both the Canadian general public (MI: 0.0091; original: 0.0035) and patients with breast cancer (MI: 0.0169; original: 0.0066).

DISCUSSION AND CONCLUSIONS

We provide 1) the first MI value sets for the EQ-5D-5L and 2) code to construct MI value sets while accounting for state-level model misspecification. Our study suggests that ignoring parameter uncertainty in value sets leads to falsely narrow SEs.

HIGHLIGHTS

Value sets for health state utility instruments are estimated subject to parameter uncertainty; this parameter uncertainty may exceed the minimum important difference of the instrument, yet it is not fully captured using current methods.This study creates the first multiply imputed value set for a multiattribute utility instrument, the EQ-5D-5L, to fully capture this parameter uncertainty.We apply the multiply imputed value set to 2 data sets from 1) the Canadian general public and 2) women with invasive breast cancer.Scoring the EQ-5D-5L using a multiply imputed value set led to wider standard error estimates, suggesting that the current practice of ignoring parameter uncertainty in the value set leads to falsely low standard errors.Our work will be of interest to methodologists and developers of the EQ-5D-5L and users of the EQ-5D-5L, such as health economists, researchers, and policy makers.

Developing the Breast Utility Instrument to Measure Health-Related Quality-of-Life Preferences in Patients with Breast Cancer: Selecting the Item for Each Dimension

Introduction. Generic preference-based instruments inadequately measure breast cancer (BrC) health-related quality-of-life preferences given advances in therapy. Our overall purpose is to develop the Breast Utility Instrument (BUI), a BrC-specific preference-based instrument. This study describes the selection of the BUI items. Methods. A total of 408 patients from diverse BrC health states completed the EORTC QLQ-C30 and BR45 (breast module). For each of 10 dimensions previously assessed with confirmatory factor analysis, we evaluated data fit to the Rasch model based on global model and item fit, including threshold ordering, item residuals, infit and outfit, differential item functioning (age), and unidimensionality. Misfitting items were removed iteratively, and the model fit was reassessed. From items fitting the Rasch model, we selected 1 item per dimension based on high patient- and clinician-rated item importance, breadth of item thresholds, and clinical relevance. Results. Global model fit was good in 7 and borderline in 3 dimensions. Separation index was acceptable in 4 dimensions. Item selection criteria were maximized for the following items: 1) physical functioning (trouble taking a long walk), 2) emotional functioning (worry), 3) social functioning (interfering with social activities), 4) pain (having pain), 5) fatigue (tired), 6) body image (dissatisfied with your body), 7) systemic therapy side effects (hair loss), 8) sexual functioning (interest in sex), 9) breast symptoms (oversensitive breast), and 10) endocrine therapy symptoms (problems with your joints). Conclusions. We propose 10 items for the BUI. Our next steps include assessing the measurement properties prior to eliciting preference weights of the BUI.

Highlights

A previous confirmatory factor analysis established 10 dimensions of the European Organisation for Research and Treatment of Cancer (EORTC) core quality of life questionnaire (QLQ-C30) and its breast module (BR45).In this study, we selected 1 item per dimension based on fit to the Rasch model, patient- and clinician-rated item importance, breadth of item thresholds, and clinical relevance.These items form the core of the future Breast Utility Instrument (BUI).The future BUI will be a novel breast cancer-specific preference-based instrument that potentially will better reflect women's preferences in clinical decision making and cost utility analyses.