Mapping Health-Related Quality of Life (HRQoL) Measures to Preference Based Measures in Metastatic Hormone Sensitive Prostate Cancer (mHSPC): A systematic literature review
Author(s)
Elaine Gallagher, MSc1, Noman Paracha, MSc1, Stephen A. Mitchell, PhD2, Alka Singh, MSc2;
1Bayer Pharmaceuticals, Basel, Switzerland, 2Mtech, Bicester, Oxfordshire, United Kingdom
1Bayer Pharmaceuticals, Basel, Switzerland, 2Mtech, Bicester, Oxfordshire, United Kingdom
Presentation Documents
OBJECTIVES: Patient-reported outcome measures represent important endpoints in mHSPC trials, with the Functional Assessment of Cancer Therapy- Prostate (FACT P) and Brief Pain Inventory (BPI) being commonly used. However, these instruments do not allow for the calculation of preference-based health, or utility values, which assist decision-makers in resource allocation. The objective of this review was to identify mapping algorithms to convert FACT-P and BPI data to preference-based measures in mHSPC patients.
METHODS: Comprehensive searches were conducted using Embase, Medline, and Evidence-Based Medicine Reviews databases from inception to December 13th, 2023. Additional searches of conferences and global health technology assessment (HTA) bodies were conducted. Identified algorithms were ‘quality assessed’ via a published checklist.
RESULTS: No studies mapped condition-specific, non-preference-based measures to preference-based measures in mHSPC. As a result, mapping algorithms in broader prostate cancer populations were considered. Seven studies relating to four unique mapping algorithms were identified in metastatic castration-resistant prostate cancer (mCRPC). All algorithms mapped FACT-P to EQ-5D (EQ-5D-3L, n=3) and employed a variety of datasets from multinational trials (observational, n=3; randomised controlled trial, n=1) and used UK preference weights to estimate EQ-5D. All studies explored multiple mapping models using regression methods; in general, ordinary least squares regression models were the best performing. Only one of the algorithms had been externally validated in a broader population. While three of the algorithms had been used in previous NICE submissions (TA259, TA316, and TA387), results were presented in scenario analysis only. No studies were found that mapped to BPI in prostate cancer.
CONCLUSIONS: A small number (n=4) of mapping algorithms were identified in mCRPC, highlighting a need for mapping algorithms to be validated in prostate cancer populations outside of this indication. To explore uncertainty in utility parameters and support decision-making, data mapped from non-preference-based measures is valuable when presented as a scenario analysis in HTA.
METHODS: Comprehensive searches were conducted using Embase, Medline, and Evidence-Based Medicine Reviews databases from inception to December 13th, 2023. Additional searches of conferences and global health technology assessment (HTA) bodies were conducted. Identified algorithms were ‘quality assessed’ via a published checklist.
RESULTS: No studies mapped condition-specific, non-preference-based measures to preference-based measures in mHSPC. As a result, mapping algorithms in broader prostate cancer populations were considered. Seven studies relating to four unique mapping algorithms were identified in metastatic castration-resistant prostate cancer (mCRPC). All algorithms mapped FACT-P to EQ-5D (EQ-5D-3L, n=3) and employed a variety of datasets from multinational trials (observational, n=3; randomised controlled trial, n=1) and used UK preference weights to estimate EQ-5D. All studies explored multiple mapping models using regression methods; in general, ordinary least squares regression models were the best performing. Only one of the algorithms had been externally validated in a broader population. While three of the algorithms had been used in previous NICE submissions (TA259, TA316, and TA387), results were presented in scenario analysis only. No studies were found that mapped to BPI in prostate cancer.
CONCLUSIONS: A small number (n=4) of mapping algorithms were identified in mCRPC, highlighting a need for mapping algorithms to be validated in prostate cancer populations outside of this indication. To explore uncertainty in utility parameters and support decision-making, data mapped from non-preference-based measures is valuable when presented as a scenario analysis in HTA.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
PCR114
Topic
Patient-Centered Research
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Oncology