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

Teresa C O Tsui; Maureen E Trudeau; Nicholas Mitsakakis; Murray D Krahn; Aileen M Davis

doi:10.1177/23814683221142267

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

MDM Policy Pract. 2022 Dec 8;7(2):23814683221142267. doi: 10.1177/23814683221142267. eCollection 2022 Jul-Dec.

Authors

Teresa C O Tsui^{1

2

3}, Maureen E Trudeau⁴, Nicholas Mitsakakis^{5

6}, Murray D Krahn¹, Aileen M Davis⁷

Affiliations

¹ Toronto Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto, Ontario, Canada.
² Canadian Centre for Applied Research in Cancer Control.
³ Hospital for Sick Children, Toronto, Ontario, Canada.
⁴ Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
⁵ Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada.
⁶ Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
⁷ Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.

Abstract

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.

Keywords: Rasch analysis; breast cancer; condition-specific preference-based instrument; health-related quality of life; item selection.