1. Potential roles of PP in industry processes | |
1.1 Early development | |
• Informing ‘go/no-go’ decisions (e.g. internal prioritization portfolio decisions) [24] | |
• Informing resource allocation decisions among multiple diseases [24] | |
• Influencing which medical product will be developed [24] | |
• Informing the design of a target product profile [14, 19, 27,28,29] | |
1.2 Clinical trial design | |
• Quantifying how clinical outcomes, benefits and risks are perceived [14, 19, 30,31,32,33,34] | |
• Indicating which clinical endpoints are of highest importance to patients [14, 31,32,33, 35] | |
• Indicating which endpoints should (not) be considered [31] | |
• Informing enrollment criteria and sample populations [19, 31, 33] | |
• Informing clinical trial sample size [27] | |
• Calculating acceptable levels of uncertainty (significance level and power) [36] | |
• Defining subgroups with different benefit-risk trade-offs [19, 24, 37] | |
1.4 Post-marketing | |
• Subgroup PP information for suggesting new markets for present indications [37] | |
• Subgroup PP information for pointing to specific treatment opportunities [37] | |
• Informing new innovations [14] | |
• Informing expanded indications or populations [14] | |
• Informing risk assessments underlying product recalls [19] | |
• Optimizing promotional materials [19] | |
• Planning and evaluating BRAs and risk management [39] | |
2. Potential roles of PP in BRA/MA | |
• Highlighting differences in views between patients and decision-makers [19, 24, 40,41,42] | |
• Highlighting situations with need for transparent communication about decision [42] | |
• Providing quantitative measures of how patients view their choices [24] | |
• Weighing (clinical) outcomes and attributes [14, 19, 25, 30, 34, 37, 38, 40, 43,44,45,46,47,48] | |
• Identifying most relevant outcomes to patients [14, 19, 24, 26, 37, 48, 49] | |
• Identifying outcomes with less perceived meaning [50] | |
• Providing insights into patient perspectives on other aspects of treatment (e.g. dosing) [34] | |
• Indicating patient benefit-risk trade-offs [18, 19, 24, 26, 34, 37, 38, 45, 47, 49, 51] | |
• Indicating whether patients are likely to use therapy if approved [41] | |
• Indicating how patients compare benefits and risks between treatment options [24] | |
• Indicating how patients weigh benefits and risks as the disease progresses [24] | |
• Enabling quantitative benefit-risk modelling in complex cases [19, 36, 37] | |
• Understanding patient heterogeneity [14, 19, 24, 37, 40, 42, 45, 52, 53] | |
• Tailoring MA decision based on subgroups with homogeneous preferences [14, 37, 42, 45] | |
3. Potential roles of PP in HTA/reimbursement | |
• Indicating patients’ preferred treatments/technologies/healthcare services [54,55,56,57] | |
• Indicating patients’ preferred health states (quality of life) [52] | |
• Indicating patients’ preferred mode of administration [52, 56] | |
• Indicating patients’ preferred clinical outcomes (including benefits/risks) [30, 50, 52] | |
• Highlighting potential differences in views between patients and decision-makers [40] | |
• Selecting, prioritizing or weighing endpoints and criteria [15, 18, 30, 44, 47, 50, 58] | |
• Highlighting the value of a treatment when the QALY is considered too narrow [59] | |
• Estimating willingness to pay or willingness to accept compensation [54] | |
• Predicting uptake rates [54] | |
• Indicating the general acceptability of a technology to patients [19, 56, 60] | |
• Providing input for economic evaluations (e.g. cost-utility analyses) [30, 47, 50, 53, 54, 61] | |
• Contributing to prioritization of topics for HTA [30] | |
• Identifying heterogeneity and segments of the patient population [52, 53] | |
• Tailoring reimbursement decisions based upon preference heterogeneity [52] |