The relevance of fatigue to relapse rate in multiple sclerosis: Applying patient preference data to the OPTIMUM trial.

BACKGROUND: In the OPTIMUM trial in patients with relapsing MS, treatment differences in annualized relapse rate (ARR, 0.088) and change in fatigue at week 108 (3.57 points, measured using the Fatigue Symptoms and Impacts Questionnaire-Relapsing Multiple Sclerosis, symptom domain (FSIQ-RMS-S)) favored ponesimod over teriflunomide. However, the importance of the fatigue outcome to patients was unclear. OBJECTIVE: To assess the importance of the OPTIMUM FSIQ-RMS-S results using data from an MS discrete choice experiment (DCE). METHODS: The DCE included components to correlate levels of physical and cognitive fatigue with FSIQ-RMS-S scores. Changes in relapses/year and time to MS progression equivalent to the treatment difference in fatigue in OPTIMUM were determined for similar fatigue levels as mean baseline fatigue in OPTIMUM. RESULTS: DCE participants would accept 0.06 more relapses/year or a 0.15-0.17 year decrease in time to MS progression for a 3.57-point difference in physical fatigue on the FSIQ-RMS-S. To improve cognitive fatigue by 3.57-points on the FSIQ-RMS-S, DCE participants would accept 0.09-0.10 more relapses/year or a 0.24-0.28 year decrease in time to MS progression. CONCLUSION: MS patients would accept 0.06 more relapses/year to change their fatigue by a similar magnitude as the between-treatment difference observed in the OPTIMUM trial.

Illustrating Emerging Good Practices for Quantitative Benefit-Risk Assessment: A Hypothetical Case Study of Systemic Biologic Treatments for Plaque Psoriasis.

OBJECTIVES: Quantitative benefit-risk assessment (qBRA) is a structured process to evaluate the benefit-risk balance of treatment options to support decision making. The ISPOR qBRA Task Force was recently established to provide recommendations for the design, conduct, and reporting of qBRA. This report presents a hypothetical case study illustrating how to apply the Task Force's recommendations toward a qBRA to inform the benefit-risk assessment of brodalumab at the time of initial marketing approval. The qBRA evaluated 2 dosing regimens of brodalumab (210 mg or 140 mg twice weekly) compared with weight-based dosing of ustekinumab and placebo. METHODS: We followed the 5 steps recommended by the Task Force. Attributes included treatment response (≥75% improvement in Psoriasis Area and Severity Index), suicidal ideation and behavior, and infections. Performance data were drawn from pivotal clinical trials of brodalumab. The qBRA used multicriteria decision analysis and preference weights from a hypothetical discrete choice experiment. Sensitivity analyses examined the robustness of benefit-risk ranking to uncertainty in clinical effect and preference estimates, consideration of a subgroup (nail psoriasis), and the maintenance phase of treatment (52 weeks instead of 12). RESULTS: Results from this hypothetical qBRA suggest that brodalumab 210 mg had a more favorable benefit-risk profile compared with ustekinumab and placebo. Ranking of brodalumab compared with ustekinumab was dependent on brodalumab's dose. Sensitivity analyses demonstrated robustness of benefit-risk ranking to uncertainty in clinical effect and preference estimates, as well as choice of attributes and length of follow-up. CONCLUSION: This case study demonstrates how to implement the ISPOR Task Force's good practice recommendations on qBRA.

Quantitative Benefit-Risk Assessment in Medical Product Decision Making: A Good Practices Report of an ISPOR Task Force.

Benefit-risk assessment is commonly conducted by drug and medical device developers and regulators, to evaluate and communicate issues around benefit-risk balance of medical products. Quantitative benefit-risk assessment (qBRA) is a set of techniques that incorporate explicit outcome weighting within a formal analysis to evaluate the benefit-risk balance. This report describes emerging good practices for the 5 main steps of developing qBRAs based on the multicriteria decision analysis process. First, research question formulation needs to identify the needs of decision makers and requirements for preference data and specify the role of external experts. Second, the formal analysis model should be developed by selecting benefit and safety endpoints while eliminating double counting and considering attribute value dependence. Third, preference elicitation method needs to be chosen, attributes framed appropriately within the elicitation instrument, and quality of the data should be evaluated. Fourth, analysis may need to normalize the preference weights, base-case and sensitivity analyses should be conducted, and the effect of preference heterogeneity analyzed. Finally, results should be communicated efficiently to decision makers and other stakeholders. In addition to detailed recommendations, we provide a checklist for reporting qBRAs developed through a Delphi process conducted with 34 experts.

Appraising patient preference methods for decision-making in the medical product lifecycle: an empirical comparison.

BACKGROUND: Incorporating patient preference (PP) information into decision-making has become increasingly important to many stakeholders. However, there is little guidance on which patient preference assessment methods, including preference exploration (qualitative) and elicitation (quantitative) methods, are most suitable for decision-making at different stages in the medical product lifecycle (MPLC). This study aimed to use an empirical approach to assess which attributes of PP assessment methods are most important, and to identify which methods are most suitable, for decision-makers' needs during different stages in the MPLC. METHODS: A four-step cumulative approach was taken: 1) Identify important criteria to appraise methods through a Q-methodology exercise, 2) Determine numerical weights to ascertain the relative importance of each criterion through an analytical hierarchy process, 3) Assess the performance of 33 PP methods by applying these weights, consulting international health preference research experts and review of literature, and 4) Compare and rank the methods within taxonomy groups reflecting their similar techniques to identify the most promising methods. RESULTS: The Q-methodology exercise was completed by 54 stakeholders with PP study experience, and the analytical hierarchy process was completed by 85 stakeholders with PP study experience. Additionally, 17 health preference research experts were consulted to assess the performance of the PP methods. Thirteen promising preference exploration and elicitation methods were identified as likely to meet decision-makers' needs. Additionally, eight other methods that decision-makers might consider were identified, although they appeared appropriate only for some stages of the MPLC. CONCLUSIONS: This transparent, weighted approach to the comparison of methods supports decision-makers and researchers in selecting PP methods most appropriate for a given application.

Something Is Better Than Nothing: The Value of Active Intervention in Stated Preferences for Treatments to Delay Onset of Alzheimer's Disease Symptoms.

BACKGROUND: The objective of the study was to understand respondents' willingness to accept hypothetical treatment-related risks in return for the benefit of additional time with normal memory from potential Alzheimer's disease interception therapies. METHODS: A US web-based discrete-choice survey was administered to respondents ages 60 to 85 years with no Alzheimer's disease diagnosis and no cognitive symptoms. Choice questions required respondents to indicate whether they preferred a constant, no-treatment condition described as 4 years of normal memory followed by 3 years of cognitive impairment and 5 years of dementia or an interception treatment with chosen risks of disabling stroke and death, but with increased duration of normal memory. The study design included internal validity tests to verify data quality. RESULTS: On average, respondents were willing to accept a 5% to 13% risk of stroke or death in the first year for treatments that could provide 1 or more additional years with normal memory. Nevertheless, 30% of respondents failed a simple internal-validity test question where the treatment alternative offered no improvement in disease progression but had significant side effects. These respondents also were more likely to choose active treatment in the subsequent series of choice questions. This unexpected finding is consistent with hopeful attitudes of patients with debilitating and potentially fatal conditions. CONCLUSION: Pro-treatment attitudes are clinically relevant and can affect the analysis and interpretation of stated-preference data. Internal-validity tests generally are underutilized in preference research. This study demonstrated how analysis of apparent validity failures can yield important insights about patient preferences.

Opportunities and challenges for the inclusion of patient preferences in the medical product life cycle: a systematic review.

BACKGROUND: The inclusion of patient preferences (PP) in the medical product life cycle is a topic of growing interest to stakeholders such as academics, Health Technology Assessment (HTA) bodies, reimbursement agencies, industry, patients, physicians and regulators. This review aimed to understand the potential roles, reasons for using PP and the expectations, concerns and requirements associated with PP in industry processes, regulatory benefit-risk assessment (BRA) and marketing authorization (MA), and HTA and reimbursement decision-making. METHODS: A systematic review of peer-reviewed and grey literature published between January 2011 and March 2018 was performed. Consulted databases were EconLit, Embase, Guidelines International Network, PsycINFO and PubMed. A two-step strategy was used to select literature. Literature was analyzed using NVivo (QSR international). RESULTS: From 1015 initially identified documents, 72 were included. Most were written from an academic perspective (61%) and focused on PP in BRA/MA and/or HTA/reimbursement (73%). Using PP to improve understanding of patients' valuations of treatment outcomes, patients' benefit-risk trade-offs and preference heterogeneity were roles identified in all three decision-making contexts. Reasons for using PP relate to the unique insights and position of patients and the positive effect of including PP on the quality of the decision-making process. Concerns shared across decision-making contexts included methodological questions concerning the validity, reliability and cognitive burden of preference methods. In order to use PP, general, operational and quality requirements were identified, including recognition of the importance of PP and ensuring patient understanding in PP studies. CONCLUSIONS: Despite the array of opportunities and added value of using PP throughout the different steps of the MPLC identified in this review, their inclusion in decision-making is hampered by methodological challenges and lack of specific guidance on how to tackle these challenges when undertaking PP studies. To support the development of such guidance, more best practice PP studies and PP studies investigating the methodological issues identified in this review are critically needed.

A Framework for Incorporating Patient Preferences Regarding Benefits and Risks into Regulatory Assessment of Medical Technologies.

BACKGROUND: In response to 2012 guidance in which the US Food and Drug Administration's (FDA) Center for Devices and Radiological Health (CDRH) stated the importance of patient-centric measures in regulatory benefit-risk assessments, the Medical Device Innovation Consortium (MDIC) initiated a project. The project was used to develop a framework to help the Food and Drug Administration (FDA) and industry sponsors understand how patient preferences regarding benefit and risk might be integrated into the review of innovative medical devices. METHODS: A public-private partnership of experts from medical device industry, government, academia and non-profits collaborated on development of the MDIC patient centered benefit-risk framework. RESULTS: The MDIC Framework examines what patient preference information is and the potential use and value of patient preference information in the regulatory process and across the product development life cycle. The MDIC Framework also includes a catalog of patient preference assessment methods and an agenda for future research to advance the field. CONCLUSIONS: This article discusses key concepts in patient preference assessment of particular importance for regulators and researchers that are addressed in the MDIC Framework for patient centered benefit-risk assessment as well as the unique public-private collaboration that led its development.

Benefit-Risk Assessment, Communication, and Evaluation (BRACE) throughout the life cycle of therapeutic products: overall perspective and role of the pharmacoepidemiologist.

PURPOSE: Optimizing a therapeutic product's benefit-risk profile is an on-going process throughout the product's life cycle. Different, yet related, benefit-risk assessment strategies and frameworks are being developed by various regulatory agencies, industry groups, and stakeholders. This paper summarizes current best practices and discusses the role of the pharmacoepidemiologist in these activities, taking a life-cycle approach to integrated Benefit-Risk Assessment, Communication, and Evaluation (BRACE). METHODS: A review of the medical and regulatory literature was performed for the following steps involved in therapeutic benefit-risk optimization: benefit-risk evidence generation; data integration and analysis; decision making; regulatory and policy decision making; benefit-risk communication and risk minimization; and evaluation. Feedback from International Society for Pharmacoepidemiology members was solicited on the role of the pharmacoepidemiologist. The case example of natalizumab is provided to illustrate the cyclic nature of the benefit-risk optimization process. RESULTS: No single, globally adopted benefit-risk assessment process exists. The BRACE heuristic offers a way to clarify research needs and to promote best practices in a cyclic and integrated manner and highlight the critical importance of cross-disciplinary input. Its approach focuses on the integration of BRACE activities for risk minimization and optimization of the benefit-risk profile. CONCLUSION: The activities defined in the BRACE heuristic contribute to the optimization of the benefit-risk profile of therapeutic products in the clinical world at both the patient and population health level. With interdisciplinary collaboration, pharmacoepidemiologists are well suited for bringing in methodology expertise, relevant research, and public health perspectives into the BRACE process.

Benefit-risk assessment of vaccines.

Benefit-risk assessment (BRA) is critical for decision-making throughout the vaccine life cycle. It requires scientific assessment of evidence to make an informed judgment on whether the vaccine has a favourable benefit-risk profile i.e. the benefits of the vaccine outweigh its risks for use in its intended indication. The assessment must also consider data gaps and uncertainties, using sensitivity analyses to show the impact of these uncertainties in the assessment. The BRA field has advanced considerably over the past years, including the use of structured BRA frameworks, quantitative BRA models and use of the patient experience data. Analytical tools and procedures to standardize BRA implementation have become increasingly important. A Benefit-Risk Assessment Module has been prepared to enable the planning, assessment, and communication of relevant BRA information via a structured B-R framework. The module can help facilitate the conduct and communication of defensible BRAs by vaccine developers, funders, regulators and policy makers in high, middle or low-income countries, both for regulatory submissions and in public health responses to infectious diseases, including for epidemics.

How to Present a Decision Object in Health Preference Research: Attributes and Levels, the Decision Model, and the Descriptive Framework.

In health preference research (HPR) studies, data are generated by participants'/subjects' decisions. When developing an HPR study, it is therefore important to have a clear understanding of the components of a decision and how those components stimulate participant behavior. To obtain valid and reliable results, study designers must sufficiently describe the decision model and its components. HPR studies require a detailed examination of the decision criteria, detailed documentation of the descriptive framework, and specification of hypotheses. The objects that stimulate subjects' decisions in HPR studies are defined by attributes and attribute levels. Any limitations in the identification and presentation of attributes and levels can negatively affect preference elicitation, the quality of the HPR data, and study results. This practical guide shows how to link the HPR question to an underlying decision model. It covers how to (1) construct a descriptive framework that presents relevant characteristics of a decision object and (2) specify the research hypotheses. The paper outlines steps and available methods to achieve all this, including the methods' advantages and limitations.