Understanding Patient Preferences: A Novel Meta-Analysis Framework for Informed Healthcare Decision
Author(s)
Matteo Bracco, MSc1, Martina Di Blasio, MSc1, Emanuele Pietropaolo, MSc1, Alessia Visconti, PhD1, Ileana Baldi, PhD2, PAOLA BERCHIALLA, PhD1.
1University of Torino, Torino, Italy, 2University of Padova, Padova, Italy.
1University of Torino, Torino, Italy, 2University of Padova, Padova, Italy.
OBJECTIVES: This study aims to investigate methodologies for the quantitative synthesis of patient preference studies (PPS), which are crucial for understanding how patients weigh treatment benefits against risks. The goal is to enhance the utility of PPS for decision-makers who increasingly rely on such data to guide health-related decisions.
METHODS: To conduct a meta-analysis of PPS, we developed a novel methodology focused on aggregating preference weights across studies. Our approach is based on Utility Theory. Specifically, we re-scaled preference weights from each study to a common utility scale. Re-scaled weights were then aggregated to produce Meta-Preference Weights (MPWs), which represent a standardized measure of attribute importance across studies. To demonstrate the applicability of this methodology, we conducted a case study synthesizing preference data from PPS on Multiple Myeloma. Data on attributes were extracted, re-scaled, and included in the meta-analysis to calculate MPWs.
RESULTS: The meta-analysis yielded MPWs for all attributes included in the PPS. The three most important were Pain (MPW = 10.722), Treatment Response (MPW = 10.306), and Life Expectancy (MPW = 9.753). Additionally, MPWs were used to derive Maximum Acceptable Risks (MARs) for risk-benefit tradeoffs. For example, patients were willing to accept up to a 0.9% risk of severe side effects for a treatment offering a significant improvement in Pain. Similarly, the MAR for a substantial increase in Life Expectancy was estimated at a 13.7% risk of severe side effects.
CONCLUSIONS: MPWs are a powerful and versatile tool that enables comprehensive evaluation of attributes across diverse PPS. Decision-makers can use them to rank attributes by their importance and assess MARs for risk/benefit pairs not covered in individual studies. This approach ensures that patient preferences are thoroughly integrated into the decision-making process, ultimately leading to more evidence-based and patient-centered healthcare decisions.
METHODS: To conduct a meta-analysis of PPS, we developed a novel methodology focused on aggregating preference weights across studies. Our approach is based on Utility Theory. Specifically, we re-scaled preference weights from each study to a common utility scale. Re-scaled weights were then aggregated to produce Meta-Preference Weights (MPWs), which represent a standardized measure of attribute importance across studies. To demonstrate the applicability of this methodology, we conducted a case study synthesizing preference data from PPS on Multiple Myeloma. Data on attributes were extracted, re-scaled, and included in the meta-analysis to calculate MPWs.
RESULTS: The meta-analysis yielded MPWs for all attributes included in the PPS. The three most important were Pain (MPW = 10.722), Treatment Response (MPW = 10.306), and Life Expectancy (MPW = 9.753). Additionally, MPWs were used to derive Maximum Acceptable Risks (MARs) for risk-benefit tradeoffs. For example, patients were willing to accept up to a 0.9% risk of severe side effects for a treatment offering a significant improvement in Pain. Similarly, the MAR for a substantial increase in Life Expectancy was estimated at a 13.7% risk of severe side effects.
CONCLUSIONS: MPWs are a powerful and versatile tool that enables comprehensive evaluation of attributes across diverse PPS. Decision-makers can use them to rank attributes by their importance and assess MARs for risk/benefit pairs not covered in individual studies. This approach ensures that patient preferences are thoroughly integrated into the decision-making process, ultimately leading to more evidence-based and patient-centered healthcare decisions.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MSR210
Topic
Methodological & Statistical Research, Real World Data & Information Systems, Study Approaches
Disease
Oncology