A Roadmap for Improving Medicare's Application of Coverage with Evidence Development.

The Centers for Medicare & Medicaid Services' (CMS) coverage with evidence development (CED) policy allows the agency to provide coverage for an item or service through a National Coverage Determination (NCD) conditional upon an agreement to collect evidence designed to address specific questions or uncertainties. The goals of this policy are to expedite beneficiary access to new items and services and to generate additional evidence on the impact of these items or services for Medicare beneficiaries. However, these goals have not been fully realized because of several issues with the way the policy has been implemented, including (1) a lack of clear criteria for when CED will be applied, (2) examples of CED data collection activities placing unnecessary burdens on clinicians and the potential for undue inducement on beneficiaries, and (3) a lack of clarity around the process and timeline for reconsidering and ending CED requirements. Additionally, there are cases where the application of CED has failed to improve access to services for certain Medicare beneficiaries because no data collection activity was implemented in response to the CED requirement or because the NCD only allows the technology to be provided and studied in certain centers of excellence. We describe a roadmap for addressing these issues which includes, for example, developing a framework to guide the application of coverage constraints in NCDs with CED requirements. Once these issues are addressed, CMS could consider expanding the use of CED to technologies that are not subject to NCDs.

HTA community perspectives on the use of patient preference information: lessons learned from a survey with members of HTA bodies.

This research sought to assess whether and how patient preference (PP) data are currently used within health technology assessment (HTA) bodies and affiliated organizations involved in technology/drug appraisals and assessments. An exploratory survey was developed by the PP Project Subcommittee of the HTA International Patient and Citizen Involvement Interest Group to gain insight into the use, impact, and role of PP data in HTA, as well as the perceived barriers to its incorporation. Forty members of HTA bodies and affiliated organizations from twelve countries completed the online survey. PP data were reported to be formally considered as part of the HTA evidence review process by 82.5 percent of the respondents, while 39.4 percent reported that most of the appraisals and assessments within their organization in the past year had submitted PP data. The leading reason for why PP data were not submitted in most assessments was time/resource constraints followed by lack of clarity on PP data impact. Participants reported that PP data had a moderate level of influence on the deliberative process and outcome of the decision, but a higher level of influence on the decision's quality. Most (81.8 percent) felt patient advocacy groups should be primarily responsible for generating and submitting this type of evidence. Insights from the survey confirm the use of PP data in HTA but reveal barriers to its broader and more meaningful integration. Encouragingly, participants believe obstacles can be overcome, paving the way for a second phase of research involving in-depth collaborative workshops with HTA representatives.

Patient-Centered Clinical Trial Design for Heart Failure Devices via Bayesian Decision Analysis.

BACKGROUND: The statistical significance of clinical trial outcomes is generally interpreted quantitatively according to the same threshold of 2.5% (in one-sided tests) to control the false-positive rate or type I error, regardless of the burden of disease or patient preferences. The clinical significance of trial outcomes-including patient preferences-are also considered, but through qualitative means that may be challenging to reconcile with the statistical evidence. OBJECTIVE: We aimed to apply Bayesian decision analysis to heart failure device studies to choose an optimal significance threshold that maximizes the expected utility to patients across both the null and alternative hypotheses, thereby allowing clinical significance to be incorporated into statistical decisions either in the trial design stage or in the post-trial interpretation stage. In this context, utility is a measure of how much well-being the approval decision for the treatment provides to the patient. METHODS: We use the results from a discrete-choice experiment study focusing on heart failure patients' preferences, questioning respondents about their willingness to accept therapeutic risks in exchange for quantifiable benefits with alternative hypothetical medical device performance characteristics. These benefit-risk trade-off data allow us to estimate the loss in utility-from the patient perspective-of a false-positive or false-negative pivotal trial result. We compute the Bayesian decision analysis-optimal statistical significance threshold that maximizes the expected utility to heart failure patients for a hypothetical two-arm, fixed-sample, randomized controlled trial. An interactive Excel-based tool is provided that illustrates how the optimal statistical significance threshold changes as a function of patients' preferences for varying rates of false positives and false negatives, and as a function of assumed key parameters. RESULTS: In our baseline analysis, the Bayesian decision analysis-optimal significance threshold for a hypothetical two-arm randomized controlled trial with a fixed sample size of 600 patients per arm was 3.2%, with a statistical power of 83.2%. This result reflects the willingness of heart failure patients to bear additional risks of the investigational device in exchange for its probable benefits. However, for increased device-associated risks and for risk-averse subclasses of heart failure patients, Bayesian decision analysis-optimal significance thresholds may be smaller than 2.5%. CONCLUSIONS: A Bayesian decision analysis is a systematic, transparent, and repeatable process for combining clinical and statistical significance, explicitly incorporating burden of disease and patient preferences into the regulatory decision-making process.

Leveraging Patient Preference Information in Medical Device Clinical Trial Design.

Use of robust, quantitative tools to measure patient perspectives within product development and regulatory review processes offers the opportunity for medical device researchers, regulators, and other stakeholders to evaluate what matters most to patients and support the development of products that can best meet patient needs. The medical device innovation consortium (MDIC) undertook a series of projects, including multiple case studies and expert consultations, to identify approaches for utilizing patient preference information (PPI) to inform clinical trial design in the US regulatory context. Based on these activities, this paper offers a cogent review of considerations and opportunities for researchers seeking to leverage PPI within their clinical trial development programs and highlights future directions to enhance this field. This paper also discusses various approaches for maximizing stakeholder engagement in the process of incorporating PPI into the study design, including identifying novel endpoints and statistical considerations, crosswalking between attributes and endpoints, and applying findings to the population under study. These strategies can help researchers ensure that clinical trials are designed to generate evidence that is useful to decision makers and captures what matters most to patients.

Patient preferences for mitral valve regurgitation treatment: a discrete choice experiment.

INTRODUCTION: This study aimed to quantify patients' preferences for benefits and risks associated with treating degenerative mitral regurgitation (DMR) via open heart surgical repair versus a beating heart surgical approach. METHODS: A D-efficient main effects discrete choice experiment (DCE) survey with 10 choice tasks that involved trade-offs across six attributes varying between two and four levels each (procedure invasiveness, recovery intensity, risk of disabling stroke, risk of new onset atrial fibrillation, risk of symptom reappearance and risk of reintervention) was administered online to either clinically confirmed (n = 30) or self-reported DMR (n = 88) patients recruited from either cardiovascular clinics or online clinical patient databases. The error component logit (ECL) analysis combined both patient cohorts after performing a Swait-Louviere scale test. Patient trade-offs across attributes were estimated in relation to either an open-heart surgery (OHS) treatment profile or a beating heart approach. RESULTS: Patients demonstrated clear preferences across all attributes for the beating heart treatment. 76.0% (95% CI: 68.1,83.9) of patients would prefer a 'beating heart' intervention relative to the 'open heart' approach despite the higher likelihood of symptom recurrence and reintervention. In exchange for the combined net benefits associated with a 'beating heart' treatment, on average, participants were willing to accept a maximum acceptable risk (MAR) of 34.6 percentage points (95% CI: 23.8,45.4) for increased risk of symptom reappearance or 22.6 percentage points (95% CI: 14.7,30.4) increased risk of reintervention. CONCLUSION: This study of US adults with DMR provides quantitative measures of risk tolerance for tradeoffs related to repair by a beating heart approach relative to conventional open-heart surgery (standard of care). These results may inform DMR treatment choices from regulatory agencies, payers, clinicians, and patients considering a beating heart repair or treatments with similar attributes as potential new alternatives to conventional surgery.

Development of an international template to support patient submissions in Health Technology Assessments.

OBJECTIVES: To develop an international template to support patient submissions in Health Technology Assessments (HTAs). This was to be based on the experience and feedback from the implementation and use of the Scottish Medicines Consortium's (SMC) Summary Information for Patient Groups (SIP). METHODS: To gather feedback on the SMC experience, web-based surveys were conducted with pharmaceutical companies and patient groups familiar with the SMC SIP. Semistructured interviews with representatives from HTA bodies were undertaken, along with patient group discussions with those less familiar with the SIP, to explore issues around the approach. These qualitative data informed the development of an international SIP template. RESULTS: Survey data indicated that 82 percent (18 of 22 respondents) of pharmaceutical company representatives felt that the SIP was worthwhile; 88 percent (15/17) of patient group respondents found the SIP helpful. Both groups highlighted the need for additional support and guidance around plain language summaries. Further suggestions included provision of a glossary of terms and cost-effectiveness information. Patient group interviews supported the survey findings and led to the development of a new template. HTA bodies raised potential challenges around buy-in, timing, and bias connected to the SIP approach. CONCLUSIONS: The international SIP template is another approach to support deliberative processes in HTA. Although challenges remain around writing summaries for lay audiences, along with feasibility considerations for HTA bodies, the SIP approach should support more meaningful patient involvement in HTAs.

Patient-centered benefit-risk analysis of transcatheter aortic valve replacement.

Background: Aortic stenosis (AS) treatments include surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR). Choosing between SAVR and TAVR requires patients to trade-off  benefits and risks. The objective of this research was to determine which  TAVR and SAVR outcomes patients consider important, collect quantitative data about how patients weigh benefits and risks, and evaluate patients' preferences for SAVR or TAVR. Methods: Patients  were recruited from advocacy organization databases. Patients self-reported as being diagnosed with AS, and as either having received AS treatment or as experiencing AS-related physical activity limitations. An online adapted swing weighting (ASW) method - a pairwise comparison of attributes - was used to elicit attribute trade-offs from 219 patients. Survey data were used to estimate patients' weights for AS treatment attributes, which were incorporated into a quantitative benefit-risk analysis (BRA) to evaluate patients' preferences for TAVR and SAVR. Results: On average, patients put greater value on attributes that favored TAVR than SAVR. Patients' valuation of the lower mortality rate, reduced procedural invasiveness, and quicker time to return to normal quality of life associated with TAVR, offset their valuation of the time over which SAVR has been proven to work. There was substantial heterogeneity in patients' preferences. This was partly explained by age, with differences in preference observed between patients <60 years to those ≥60 years. A Monte Carlo Simulation found that 79.5% of patients prefer TAVR. Conclusions: Most AS patients are willing to tolerate sizable increases in clinical risk in exchange for the benefits of TAVR, resulting in a large proportion of patients preferring TAVR to SAVR. Further work should be undertaken to characterize the heterogeneity in preferences for AS treatment attributes. Shared decision-making tools based on attributes important to patients can support patients' selection of the procedure that best meets their needs.