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.
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.
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.
What you need to know: The recommendations apply to patients under 60 years old with patent foramen ovale (PFO) who have had a cryptogenic ischaemic stroke, when extensive workup for other ...aetiologies of stroke is negative; For patients who are open to all options, we make a weak recommendation for PFO closure plus antiplatelet therapy rather than anticoagulant therapy; For patients in whom anticoagulation is contraindicated or declined, we make a strong recommendation for PFO closure plus antiplatelet therapy versus antiplatelet therapy alone; For patients in whom closure is contraindicated or declined, we make a weak recommendation for anticoagulant therapy rather than antiplatelet therapy; Further research may alter the recommendations that involve anticoagulant therapy. Options for the secondary prevention of stroke in patients younger than 60 years who have had a cryptogenic ischaemic stroke thought to be secondary to patent foramen ovale (PFO) include PFO closure (with antiplatelet therapy), antiplatelet therapy alone, or anticoagulants. International guidance and practice differ on which option is preferable. The BMJ Rapid Recommendations panel used a linked systematic review1 triggered by three large randomised trials published in September 2017 that suggested PFO closure might reduce the risk of ischaemic stroke more than alternatives.234 The panel felt that the studies, when considered in the context of the full body of evidence, might change current clinical practice.5 The linked systematic review finds that PFO closure prevents recurrent stroke relative to antiplatelet therapy, but possibly not relative to anticoagulants, and is associated with procedural complications and persistent atrial fibrillation.1 The review also presents evidence regarding the role of anticoagulants or antiplatelet therapy when PFO closure is not acceptable or is contraindicated.
The use of digital health products has gained considerable interest as a new way to improve therapeutic research and development. Although these products are being adopted by various industries and ...stakeholders, their incorporation in clinical trials has been slow due to a disconnect between the promises of digital products and potential risks in using these new technologies in the absence of regulatory support. The Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium hosted a public workshop to address challenges and opportunities in this field. Important characteristics of tool development were addressed in a series of presentations, case studies, and open panel sessions. The workshop participants endorsed the usefulness of an evidentiary criteria framework, highlighted the importance of early patient engagement, and emphasized the potential impact of digital monitoring tools and precompetitive collaborations. Concerns were expressed about the lack of real‐life validation examples and the limitations of legacy standards used as a benchmark for novel tool development and validation. Participants recognized the need for novel analytical and statistical approaches to accommodate analyses of these novel data types. Future directions are to harmonize definitions to build common methodologies and foster multidisciplinary collaborations; to develop approaches toward integrating digital monitoring data with the totality of the data in clinical trials, and to continue an open dialog in the community. There was a consensus that all these efforts combined may create a paradigm shift of how clinical trials are planned, conducted, and results brought to regulatory reviews.
Patient-centered clinical trial design and execution is becoming increasingly important. No best practice guidelines exist despite a key stakeholder declaration to create more effective engagement ...models. This study aims to gain a better understanding of attitudes and practices for engaging patient groups so that actionable recommendations may be developed.
Individuals from industry, academic institutions, and patient groups were identified through Clinical Trials Transformation Initiative and Drug Information Association rosters and mailing lists. Objectives, practices, and perceived barriers related to engaging patient groups in the planning, conduct, and interpretation of clinical trials were reported in an online survey. Descriptive and inferential statistical analysis of survey data followed a literature review to inform survey questions.
Survey respondents (n = 179) valued the importance of involving patient groups in research; however, patient group respondents valued their contributions to research protocol development, funding acquisition, and interpretation of study results more highly than those contributions were valued by industry and academic respondents (all p < .001). Patient group respondents placed higher value in open communications, clear expectations, and detailed contract execution than did non-patient group respondents (all p < .05). Industry and academic respondents more often cited internal bureaucratic processes and reluctance to share information as engagement barriers than did patient group respondents (all p < .01). Patient groups reported that a lack of transparency and understanding of the benefits of collaboration on the part of industry and academia were greater barriers than did non-patient group respondents (all p< .01).
Despite reported similarities among approaches to engagement by the three stakeholder groups, key differences exist in perceived barriers and benefits to partnering with patient groups among the sectors studied. This recognition could inform the development of best practices for patient-centered clinical trial design and execution. Additional research is needed to define and optimize key success factors.
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.
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.
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.
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.
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