How to integrate evidence from patient preference studies into health technology assessment: a critical review and recommendations

Abstract

Abstract Health technology assessment (HTA) agencies vary in their use of quantitative patient preference data (PP) and the extent to which they have formalized this use in their guidelines. Based on the authors' knowledge of the literature, we identified six different PP “use cases” that integrate PP into HTA in five different ways: through endpoint selection, clinical benefit rating, predicting uptake, input into economic evaluation, and a means to weight all HTA criteria. Five types of insight are distinguished across the use cases: understanding what matters to patients, predicting patient choices, estimating the utility generated by treatment benefits, estimating the willingness to pay for treatment benefits, and informing distributional considerations. Summarizing the literature on these use cases, we recommend circumstances in which PP can add value to HTA and the further research and guidance that is required to support the integration of PP in HTA. Where HTA places more emphasis on clinical outcomes, novel endpoints are available; or where there are already many treatment options, PP can add value by helping decision makers to understand what matters to patients. Where uptake is uncertain, PP can be used to estimate uptake probability. Where indication-specific utility functions are required or where existing utility measures fail to capture the value of treatments, PP can be used to generate or supplement existing utility estimates. Where patients are paying out of pocket, PP can be used to estimate willingness to pay.