Examining Endpoint Concordance in Clinical Trials and Real-World Clinical Practice to Advance Real-World Evidence Utilization.

Real-world evidence (RWE) is increasingly contributing to more informed decisions regarding the optimal access to and use of therapeutics to improve patient outcomes. However, in many cases, a disconnect between evidence derived from clinical trials and the RWE that follows market approval impedes the potential value and widespread adoption of RWE to optimize patient care. Collaborators with the Learning Ecosystems Accelerator for Patient-centered, Sustainable innovation (LEAPS), a major project of the Tufts Medical Center [formally Massachusetts Institute of Technology (MIT)] NEW Drug Development ParadIGmS (NEWDIGS) initiative, propose assessing the relationship between efficacy endpoints used in randomized controlled trials (RCTs) and effectiveness measures that inform treatment decisions within real-world clinical settings as one way to bridge this divide and further leverage RWE to improve care and patient outcomes. This commentary outlines elements of an endpoint concordance study using Rheumatoid Arthritis as a case study. The authors describe the ways in which better understanding of the relationship between effectiveness and RCT endpoints could improve the confidence in and adoption of RWE by both contextualizing existing RWE as well as identifying ways in which to improve the value of RWE in improving care and outcomes.

Extending the vision of adaptive point-of-care platform trials to improve targeted use of drug therapy regimens: An agile approach in the learning healthcare system toolkit.

OBJECTIVES: Improving the targeted use of drug regimens requires robust real-world evidence (RWE) to address the uncertainties that remain regarding their real-world performance following market entry. However, challenges in the current state of RWE production limit its impact on clinical decisions, as well as its operational scalability and sustainability. We propose an adaptive point-of-care (APoC) platform trial as an approach to RWE production that improves both clinical and operational efficiencies. METHODS AND FINDINGS: We explored design innovations, operational challenges, and infrastructure needs within a multi-stakeholder consortium to evaluate the potential of an APoC platform trial for studying chronic disease treatment regimens using rheumatoid arthritis as a case study. The concept integrates elements from adaptive clinical trials (dynamic treatment regimen strategies) and point-of-care trials (research embedded into routine clinical care) under a perpetual platform infrastructure. The necessary components to implement an APoC platform trial within outpatient settings exist, and present an opportunity for a cross-disciplinary, multi-stakeholder approach. Effective engagement of key stakeholders involved in and impacted by the platform is critical to success. Our collaborative design process identified three high-impact stakeholder-engagement areas: (1) focus on research question(s), (2) design and implementation planning such that it is feasible and fit-for-purpose, and (3) measurement, or meaningful metrics for both clinical (patient outcomes) and system (operational efficiencies) impact. CONCLUSIONS: An APoC platform trial for rheumatoid arthritis integrating innovative design elements in a scalable infrastructure has the potential to reduce important uncertainties about the real-world performance of biomedical innovations and improve clinical decisions.

Outcomes of erythropoiesis-stimulating agents in cancer patients with chemotherapy-induced anemia.

PURPOSE: To assess the clinical and economic outcomes among patients with chemotherapy-induced anemia (CIA) treated with United States Food and Drug Administration-approved fixed dosing regimens of erythropoiesis-stimulating agents (ESA). METHODS: Data were employed from the Dosing and Outcomes Study of Erythropoiesis-Stimulating Therapies (DOSE) registry to evaluate CIA patients who were initiated on either epoetin alfa (EPO) 40,000 Units (U) or darbepoetin alfa (DARB) 500 micrograms (mcg) between January 1, 2006 and May 8, 2009. Study measurements included ESA treatment dose and dose ratio, changes in hemoglobin (Hb) levels from baseline, and cumulative ESA costs. RESULTS: Five hundred forty patients treated in 44 clinical centers were evaluated, of which 420 were initiated on EPO 40,000 U and 120 were initiated on DARB 500 mcg. Both cohorts had similar baseline characteristics, although EPO patients were less likely than DARB patients to have received iron supplementation before ESA initiation (11.4% EPO vs. 20.0% DARB, p = 0.015). The EPO-to-DARB dose ratio based on cumulative ESA dose was 169:1 (U EPO: mcg DARB). EPO patients showed statistically greater Hb improvement compared to DARB patients, and compared to EPO patients, a greater proportion of DARB patients required a blood transfusion (13.9% EPO vs. 22.5% DARB, p = 0.026). Mean cumulative ESA cost was significantly lower for EPO patients than DARB patients ($4,261 EPO vs. $8,643 DARB, p < 0.0001). CONCLUSIONS: These findings reported that patients with CIA achieved more favorable clinical and economic outcomes if initiated with EPO 40,000 U vs. DARB 500 mcg.

Precision Reimbursement for Precision Medicine: Using Real-World Evidence to Evolve From Trial-and-Project to Track-and-Pay to Learn-and-Predict.

Basic scientists and drug developers are accelerating innovations toward the goal of precision medicine. Regulators create pathways for timely patient access to precision medicines, including individualized therapies. Healthcare payors acknowledge the need for change but downstream innovation for coverage and reimbursement is only haltingly occurring. Performance uncertainty, high price-tags, payment timing, and actuarial risk issues associated with precision medicines present novel financial challenges for payors. With traditional drug reimbursement frameworks, payment is based on an assumed randomized controlled trial (RCT) projection of real-world effectiveness, a "trial-and-project" strategy; the clinical benefit realized for patients is not usually ascertained ex post by collection of real-world data (RWD). To mitigate financial risks resulting from clinical performance uncertainty, manufacturers and payors devised "track-and-pay" frameworks (i.e., the tracking of a pre-agreed treatment outcome which is linked to financial consequences). Whereas some track-and-pay arrangements have been successful, inherent weaknesses include the potential for misalignment of incentives, the risk of channeling of patients, and a failure to use the RWD generated to enable continuous learning about treatments. "Precision reimbursement" (PR) intends to overcome inherent weaknesses of simple track-and-pay schemes. In combining the collection of RWD with advanced analytics (e.g., artificial intelligence and machine learning) to generate actionable real-world evidence, with prospective alignment of incentives across all stakeholders (including providers and patients), and with pre-agreed use and dissemination of information generated, PR becomes a "learn-and-predict" model of payment for performance. We here describe in detail the concept of PR and lay out the next steps to make it a reality.

A Useful and Sustainable Role for N-of-1 Trials in the Healthcare Ecosystem.

Clinicians and patients often try a treatment for an initial period to inform longer-term therapeutic decisions. A more rigorous approach involves N-of-1 trials. In these single-patient crossover trials, typically conducted in patients with chronic conditions, individual patients are given candidate treatments in a double-blinded, random sequence of alternating periods to determine the most effective treatment for that patient. However, to date, these trials are rarely done outside of research settings and have not been integrated into general care where they could offer substantial benefit. Designating this classical, N-of-1 trial design as type 1, there also are new and evolving uses of N-of-1 trials that we designate as type 2. In these, rather than focusing on optimizing treatment for chronic diseases when multiple approved choices are available, as is typical of type 1, a type 2 N-of-1 trial tests treatments designed specifically for a patient with a rare disease, to facilitate personalized medicine. While the aims differ, both types face the challenge of collecting individual-patient evidence using standard, trusted, widely accepted methods. To fulfill their potential for producing both clinical and research benefits, and to be available for wide use, N-of-1 trials will have to fit into the current healthcare ecosystem. This will require generalizable and accepted processes, platforms, methods, and standards. This also will require sustainable value-based arrangements among key stakeholders. In this article, we review opportunities, stakeholders, issues, and possible approaches that could support general use of N-of-1 trials and deliver benefit to patients and the healthcare enterprise. To assess and expand the benefits of N-of-1 trials, we propose multistakeholder meetings, workshops, and the generation of methods, standards, and platforms that would support wider availability and the value of N-of-1 trials.

Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2.

Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of network meta-analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional meta-analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting network meta-analyses (our use of this term includes most methods that involve meta-analysis in the context of a network of evidence). We start with a discussion of strategies for developing networks of evidence. Next we briefly review assumptions of network meta-analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of network meta-analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and network meta-analysis. A further section discusses eight key areas for future research.

Dynamic Dossier in the Cloud: A Sociotechnical Architecture for a Real-Time and Metrics-Based Data Tracking System with Gene and Cell Therapies as a Case Study.

BACKGROUND: Data sharing among stakeholders in the development, access, and use of drug therapies is critical but the current system and process are inefficient. METHODS: We take a Systems Engineering approach with a realistic use case to propose a scalable design for multi-stakeholder data sharing. RESULTS: We make three major contributions to the drug development and healthcare communities: first, a methodology for developing a multi-stakeholder data sharing system, with its focus on high-level requirements that influence the design of the system architecture and technology choice; second, the development of a realistic use case for long-term patient and therapy data tracking and sharing in the use of potentially curative and durable gene and cell therapies. Further, a bridge for the 'awareness gap' was found between the payer (Payer is organization which takes care of financial and operational aspects (which include insurance plans, provider network) of providing health care to US citizens. Or refer to health care insurers.) and the regulator communities by illustrating the common data tracking needs, which highlights the need for coordinated data activities; and third, a proposed system architecture for scalable, multi-stakeholder data sharing. Next steps are briefly discussed. CONCLUSION: We present a system design for multiple stakeholders such as the payer, the regulator, the developer (drug manufacturer), and the healthcare provider to share data for their decision-making. The stakeholder community would benefit from collaboratively moving the system development proposal forward for efficient and cost-effective data sharing.

A Pragmatic Clinical Trial to Compare the Real-World Effectiveness of V-Go versus Standard Delivery of Insulin in Patients with Advanced Type 2 Diabetes.

BACKGROUND: Many patients with type 2 diabetes mellitus (T2DM) do not have adequate glycemic control, leading to poor patient outcomes and high healthcare costs. OBJECTIVE: This prospective pragmatic clinical trial evaluated V-Go, a wearable insulin delivery device, compared with standard treatment optimization (STO) among insulin-treated patients with T2DM in a real-world, community-based practice setting. METHODS: Study sites, rather than individual patients, were randomized to V-Go or STO via cluster randomization. Patients were treated according to routine clinical practice and followed up to 4 months. T2DM medications and supplies were purchased utilizing usual insurance and co-pay systems. The primary analysis was an unadjusted treatment group comparison of glycosylated hemoglobinA1c (HbA1c) change from baseline to end of study (EOS). A cost of therapy analysis was completed on patients who had received comparable baseline T2DM treatment with multiple daily basal-bolus insulin injections (MDI). RESULTS: Analysis included 415 patients (169 V-Go, 246 STO) enrolled from 52 US sites. Mean baseline HbA1c (9.6%) was higher in V-Go (9.9%, range 8.0% - 14.2%) than STO (9.3%, range 7.9% - 13.9%, p <.001). HbA1c decreased from baseline to EOS in both V-Go (-1.0%, p<.001) and STO (-0.5%, p<.001); V-Go had significantly larger decrease (p=.002). V-Go had a significant reduction (p<.001) in mean insulin total daily dose (TDD; 0.76 U/kg baseline, 0.57 U/kg EOS), not seen in STO (0.72 U/kg baseline and EOS). The MDI group included 95 (56.2%) V-Go and 113 STO (45.9%) patients. Mean baseline HbA1c was significantly higher in V-Go (9.9%) than STO (9.4%). V-Go also experienced larger decrease in HbA1c from baseline (-1.0%) than STO (-0.36%) (p=.006) with a decrease in TDD, while STO TDD remained unchanged. EOS mean per patient per day cost of diabetes treatment was lower for V-Go ($30.59) vs STO ($32.20) (p=.006). V-Go was more cost effective than STO ($24.02 per 1% drop in HbA1c vs $58.86, respectively). CONCLUSIONS: This pragmatic clinical trial demonstrated improved HbA1c levels, lower cost, and decreased insulin dose in patients with T2DM initiating V-Go vs STO in a real-world community-based practice setting. Observed baseline HbAlc indicated use of V-Go in more difficult to manage diabetes patients.

Dosing and Outcomes Study of Erythropoiesis-Stimulating Therapies (DOSE) : a registry for characterizing anaemia management and outcomes in oncology patients.

BACKGROUND AND OBJECTIVE: To report the design, methodology, implementation and initial results of the Dosing and Outcomes Study of Erythropoiesis-Stimulating Therapies (DOSE) Registry, the first US patient registry to collect and report on practice patterns and outcomes associated with erythropoiesis-stimulating therapy (EST) for anaemia management in oncology patients. METHODS: DOSE is a prospective ongoing registry of oncology patients treated with epoetin-alpha or darbepoetin-alpha. Patients from either community or academic centres who meet prespecified entry criteria are eligible for inclusion in the registry. Data collected include patient demographic and clinical characteristics, EST administration, haematological parameters, patient-reported outcomes and medical resource utilization. Patients are followed from EST initiation through to the end of therapy or 16 weeks, whichever is earlier. RESULTS: Initial results from 45 sites for 861 patients (epoetin-alpha, n = 312; darbepoetin-alpha, n = 549) showed that baseline demographic and disease characteristics were similar between the two treatment groups. Administration of EST at both weekly and > or =2-weekly intervals was observed in both groups, with similar numbers of haemoglobin determinations. However, the mean number of office visits was higher in the darbepoetin-alpha group despite more frequent administration of therapy at > or =2-weekly intervals in this group. Mean treatment duration was approximately 8 weeks for both groups. Mean post-baseline haemoglobin levels of 11-12 g/dL were achieved and maintained at all timepoints assessed with epoetin-alpha but not with darbepoetin-alpha. Both groups had similar rates of packed red blood cell transfusions. CONCLUSIONS: The DOSE Registry is a valuable source of data relating to anaemia management, practice patterns and outcomes in oncology patients from the perspective of actual clinical practice. Results from this registry should provide patients, clinicians and healthcare decision makers with a better understanding of the relationship between EST dosage and outcomes in the clinical setting.