How can we optimise health technology assessment and reimbursement decisions to accelerate access to new cardiovascular medicines?

Regulatory approvals of, and subsequent access to, innovative cardiovascular medications have declined. How much of this decline relates to the final step of gaining reimbursement for new treatments is unknown. Payers and health technology assessment (HTA) bodies look beyond efficacy and safety to assess whether a new drug improves patient outcomes, quality of life, or satisfaction at a cost that is affordable compared to existing treatments. HTA bodies work within a limited healthcare budget, and this is one of the reasons why only half of newly approved drugs are accepted for reimbursement, or receive restricted or "optimised" recommendations from HTA bodies. All stakeholders have the common goal of facilitating access to safe, effective, and affordable treatments to appropriate patients. An important strategy to expedite this is providing optimal data. This is demonstrably facilitated by early (and ongoing) discussions between all stakeholders. Many countries have formal programmes to provide collaborative regulatory and HTA advice to developers. Other strategies include aligning regulatory and HTA processes, increasing use of real-world evidence, formally defining the decision-making process, and educating stakeholders on the criteria for positive decision making. Industry should focus on developing treatments for unmet medical needs, seek early engagement with HTA and regulatory bodies, improve methodologies for optimal price setting, develop internal systems to collaborate with national and international stakeholders, and conduct post-approval studies. Patient involvement in all stages of development, including HTA, is critical to capture the lived experience and priorities of those whose lives will be impacted by new treatment approvals.

Inclusion and diversity in clinical trials: Actionable steps to drive lasting change.

BACKGROUND: Improving diversity in clinical trials is essential in order to produce generalizable results. Although the importance of representation has become increasingly recognized, identifying strategies to approach this work remains elusive. This article reviews the proceedings of a multi-stakeholder conference about the current state of diversity in clinical trials and outlines actionable steps for improvement. METHODS: Conference attendees included representatives from the United States Food and Drug Administration (FDA), National Institutes of Health (NIH), practicing clinical investigators, pharmaceutical and device companies, community-based organizations, data analytics companies, and patient advocacy groups. At this virtual event, attendees were asked to consider key questions around best practices for engagement of underrepresented populations. RESULTS: Community engagement is an integral part of recruitment and retention of underrepresented groups. Decentralization of sites and use of digital tools can enhance the accessibility of clinical research. Finally, improving representation among investigators and clinical research staff may translate to diverse clinical trial participants. CONCLUSION: Improving diversity in clinical trials is an ethical and scientific imperative, which requires a multifaceted approach.

Return of individual research results: What do participants prefer and expect?

Newer data platforms offer increased opportunity to share multidimensional health data with research participants, but the preferences of participants for which data to receive and how is evolving. Our objective is to describe the preferences and expectations of participants for the return of individual research results within Project Baseline Health Study (PBHS). The PBHS is an ongoing, multicenter, longitudinal cohort study with data from four initial enrollment sites. PBHS participants are recruited from the general population along with groups enriched for heart disease and cancer disease risk. Cross-sectional data on return of results were collected in 2017-2018 from an (1) in-person enrollment survey (n = 1,890), (2) benchmark online survey (n = 1,059), and (3) participant interviews (n = 21). The main outcomes included (1) preferences for type of information to be added next to returned results, (2) participant plans for sharing returned results with a non-study clinician, and (3) choice to opt-out of receiving genetic results. Results were compared by sociodemographic characteristics. Enrollment and benchmark survey respondents were 57.1% and 53.5% female, and 60.0% and 66.2% white, respectively. Participants preferred the following data types be added to returned results in the future: genetics (29.9%), heart imaging, (16.4%), study watch (15.8%), and microbiome (13.3%). Older adults (OR 0.60, 95% CI: 0.41-0.87) were less likely to want their genetic results returned next. Forty percent of participants reported that they would not share all returned results with their non-study clinicians. Black (OR 0.64, 95% CI 0.43-0.95) and Asian (OR 0.47, 95% CI 0.30-0.73) participants were less likely, and older participants more likely (OR 1.45-1.61), to plan to share all results with their clinician than their counterparts. At enrollment, 5.8% of participants opted out of receiving their genetics results. The study showed that substantial heterogeneity existed in participant's preferences and expectations for return of results, and variations were related to sociodemographic characteristics.

Evaluation, Acceptance, and Qualification of Digital Measures: From Proof of Concept to Endpoint.

To support the successful adoption of digital measures into internal decision making and evidence generation for medical product development, we present a unified lexicon to aid communication throughout this process, and highlight key concepts including the critical role of participant engagement in development of digital measures. We detail the steps of bringing a successful proof of concept to scale, focusing on key decisions in the development of a new digital measure: asking the right question, optimized approaches to evaluating new measures, and whether and how to pursue qualification or acceptance. Building on the V3 framework for establishing verification and analytical and clinical validation, we discuss strategic and practical considerations for collecting this evidence, illustrated with concrete examples of trailblazing digital measures in the field.

Development and Application of a Patient Group Engagement Prioritization Tool for Use in Medical Product Development.

INTRODUCTION: Patient group engagement is increasingly used to inform the design, conduct, and dissemination of clinical trials and other medical research activities. However, the priorities of industry sponsors and patient groups differ, and there is currently no framework to help these groups identify mutually beneficial engagement activities. METHODS: We conducted 28 qualitative, semi-structured interviews with representatives from research sponsor organizations (n = 14) and patient groups (n = 14) to determine: (1) how representatives define benefits and investments of patient group engagement in medical product development, and (2) to refine a list of 31 predefined patient group engagement activities. RESULTS: Patient group and sponsor representatives described similar benefits: engagement activities can enhance the quality and efficiency of clinical trials by improving patient recruitment and retention, reduce costs, and help trials meet expectations of regulators and payers. All representatives indicated that investments include both dedicated staff time and expertise, and financial resources. Factors to consider when evaluating benefits and investments were also identified as were suggestions for clarifying the list of engagement activities. DISCUSSION: Using these findings, we refined the 31 engagement activities to 24 unique activities across the medical product development lifecycle. We also developed a web-based prioritization tool ( https://prioritizationtool.ctti-clinicaltrials.org/ ) to help clinical research sponsors and patient groups identify high-priority engagement activities. Use of this tool can help sponsors and patient groups identify the engagement activities that they believe will provide the most benefit for the least investment and may lead to more meaningful and mutually beneficial partnerships in medical product development.

Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health.

BACKGROUND: With the rise of connected sensor technologies, there are seemingly endless possibilities for new ways to measure health. These technologies offer researchers and clinicians opportunities to go beyond brief snapshots of data captured by traditional in-clinic assessments, to redefine health and disease. Given the myriad opportunities for measurement, how do research or clinical teams know what they should be measuring? Patient engagement, early and often, is paramount to thoughtfully selecting what is most important. Regulators encourage stakeholders to have a patient focus but actionable steps for continuous engagement are not well defined. Without patient-focused measurement, stakeholders risk entrenching digital versions of poor traditional assessments and proliferating low-value tools that are ineffective, burdensome, and reduce both quality and efficiency in clinical care and research. SUMMARY: This article synthesizes and defines a sequential framework of core principles for selecting and developing measurements in research and clinical care that are meaningful for patients. We propose next steps to drive forward the science of high-quality patient engagement in support of measures of health that matter in the era of digital medicine. KEY MESSAGES: All measures of health should be meaningful, regardless of the product's regulatory classification, type of measure, or context of use. To evaluate meaningfulness of signals derived from digital sensors, the following four-level framework is useful: Meaningful Aspect of Health, Concept of Interest, Outcome to be measured, and Endpoint (exclusive to research). Incorporating patient input is a dynamic process that requires more than a single, transactional touch point but rather should be conducted continuously throughout the measurement selection process. We recommend that developers, clinicians, and researchers reevaluate processes for more continuous patient engagement in the development, deployment, and interpretation of digital measures of health.

The Project Baseline Health Study: a step towards a broader mission to map human health.

The Project Baseline Health Study (PBHS) was launched to map human health through a comprehensive understanding of both the health of an individual and how it relates to the broader population. The study will contribute to the creation of a biomedical information system that accounts for the highly complex interplay of biological, behavioral, environmental, and social systems. The PBHS is a prospective, multicenter, longitudinal cohort study that aims to enroll thousands of participants with diverse backgrounds who are representative of the entire health spectrum. Enrolled participants will be evaluated serially using clinical, molecular, imaging, sensor, self-reported, behavioral, psychological, environmental, and other health-related measurements. An initial deeply phenotyped cohort will inform the development of a large, expanded virtual cohort. The PBHS will contribute to precision health and medicine by integrating state of the art testing, longitudinal monitoring and participant engagement, and by contributing to the development of an improved platform for data sharing and analysis.

The Path Forward for Digital Measures: Suppressing the Desire to Compare Apples and Pineapples.

Digital measures are becoming more prevalent in clinical development. Methods for robust evaluation are increasingly well defined, yet the primary barrier for digital measures to transition beyond exploratory usage often relies on a comparison to the existing standards. This article focuses on how researchers should approach the complex issue of comparing across assessment modalities. We discuss comparisons of subjective versus objective assessments, or performance-based versus behavioral measures, and we pay particular attention to the situation where the expected association may be poor or nonlinear. We propose that, rather than seeking to replace the standard, research should focus on a structured understanding of how the new measure augments established assessments, with the ultimate goal of developing a more complete understanding of what is meaningful to patients.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement.

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

Technology-Enabled Clinical Trials: Transforming Medical Evidence Generation.

The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.

The Rules of Engagement: CTTI Recommendations for Successful Collaborations Between Sponsors and Patient Groups Around Clinical Trials.

OBJECTIVE: To identify the elements necessary for successful collaboration between patient groups and academic and industry sponsors of clinical trials, in order to develop recommendations for best practices for effective patient group engagement. METHODS: In-depth interviews, informed by a previously reported survey, were conducted to identify the fundamentals of successful patient group engagement. Thirty-two respondents from 3 sectors participated: patient groups, academic researchers, and industry. The findings were presented to a multistakeholder group of experts in January 2015. The expert group came to consensus on a set of actionable recommendations for best practices for patient groups and research sponsors. RESULTS: Interview respondents acknowledged that not all patient groups are created equal in terms of what they can contribute to a clinical trial. The most important elements for effective patient group engagement include establishing meaningful partnerships, demonstrating mutual benefits, and collaborating as partners from the planning stage forward. Although there is a growing appreciation by sponsors about the benefits of patient group engagement, there remains some resistance and some uncertainty about how best to engage. Barriers include mismatched expectations and a perception that patient groups lack scientific sophistication and that "wishful thinking" may cloud their recommendations. CONCLUSIONS: Patient groups are developing diverse skillsets and acquiring assets to leverage in order to become collaborators with industry and academia on clinical trials. Growing numbers of research sponsors across the clinical trials enterprise are recognizing the benefits of continuous and meaningful patient group engagement, but there are still mindsets to change, and stakeholders need further guidance on operationalizing a new model of clinical trial conduct.

Assessing the Financial Value of Patient Engagement: A Quantitative Approach from CTTI's Patient Groups and Clinical Trials Project.

BACKGROUND: While patient groups, regulators, and sponsors are increasingly considering engaging with patients in the design and conduct of clinical development programs, sponsors are often reluctant to go beyond pilot programs because of uncertainty in the return on investment. We developed an approach to estimate the financial value of patient engagement. METHODS: Expected net present value (ENPV) is a common technique that integrates the key business drivers of cost, time, revenue, and risk into a summary metric for project strategy and portfolio decisions. We assessed the impact of patient engagement on ENPV for a typical oncology development program entering phase 2 or phase 3. RESULTS: For a pre-phase 2 project, the cumulative impact of a patient engagement activity that avoids one protocol amendment and improves enrollment, adherence, and retention is an increase in net present value (NPV) of $62MM ($65MM for pre-phase 3) and an increase in ENPV of $35MM ($75MM for pre-phase 3). Compared with an investment of $100,000 in patient engagement, the NPV and ENPV increases can exceed 500-fold the investment. This ENPV increase is the equivalent of accelerating a pre-phase 2 product launch by 2½ years (1½ years for pre-phase 3). CONCLUSIONS: Risk-adjusted financial models can assess the impact of patient engagement. A combination of empirical data and subjective parameter estimates shows that engagement activities with the potential to avoid protocol amendments and/or improve enrollment, adherence, and retention may add considerable financial value. This approach can help sponsors assess patient engagement investment decisions.

Patient engagement in clinical trials: The Clinical Trials Transformation Initiative's leadership from theory to practical implementation.

Patient engagement is an increasingly important aspect of successful clinical trials. Over the past decade, as patient group involvement in clinical trials has continued to increase and diversify, the Clinical Trials Transformation Initiative has not only recognized the crucial role patients play in improving the clinical trial enterprise but also made a deep commitment to help grow and shape the emerging field of patient engagement. This article describes the evolution of patient engagement including the origins of the patient engagement movement; barriers to successful engagement and remaining challenges to full and valuable collaboration between patient groups and trial sponsors; and Clinical Trials Transformation Initiative's role in influencing the field through organizational practices, formal project work and resulting recommendations, and external advocacy efforts.