Table 2 Potential roles of PP in the MPLC

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]

  • Defining areas of unmet medical need [14, 16, 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]

  • Analyzing clinical trials [14, 19]

  • Defining subgroups with different benefit-risk trade-offs [19, 24, 37]

 1.3 Product labelling [14, 19, 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]

  • Redesigning and improving existing products [14, 19]

  • Informing expanded indications or populations [14]

  • Informing risk assessments underlying product recalls [19]

  • Optimizing promotional materials [19]

 1.5 Pharmacovigilance activities [19, 38, 39]

  • Planning and evaluating BRAs and risk management [39]

2. Potential roles of PP in BRA/MA

 • Highlighting patients’ needs for treatment [25, 26]

 • 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]

 • Providing information on uncertainty tolerance [24, 49]

 • 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]

 • Examining relative benefit-risk trade-offs [44, 54]

 • 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]