Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective

Ghadessi, Mercedeh; Di, Junrui; Wang, Chenkun; Toyoizumi, Kiichiro; Shao, Nan; Mei, Chaoqun; Demanuele, Charmaine; Tang, Rui; McMillan, Gianna; Beckman, Robert A.

doi:10.1186/s13023-023-02693-7

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…From an operational point of view, deploying a single wrist-worn, watch-like device will increase compliance and reduce data loss due to difficulty in wearing the devices. This will be a good step further toward decentralized clinical trials and more patient-centric clinical trials in general [11,54]. However, for therapeutic areas where specific spatial and spatiotemporal features are of clinical interest, such as stride duration, step symmetry, or gait variability, it may still be preferable to use the more fit-for-purpose devices, such as the lumbar-worn devices [55].…”

Section: Discussionmentioning

confidence: 99%

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Lin,

Karahanoglu,

Psaltos

et al. 2023

Sensors

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Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant’s naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients’ comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

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Section: Discussionmentioning

confidence: 99%

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Lin,

Karahanoglu,

Psaltos

et al. 2023

Sensors

Self Cite

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“…Applying the estimand framework beyond the articulation of clinical questions of interest in specific development programs has proven useful in multiple other areas e.g., use of external controls in oncology (28), analysis of patient-reported outcomes(29), or design of decentralized studies in rare diseases. (30) Our experience using the sets of considerations leads us to believe that the biggest gain of decentralization scientifically will come from an implementation that allow us to answer a question that we could not otherwise answer well with a more traditional on-site clinical trial. If the estimand is novel and fully aligned with the decentralized component, then fitness-forpurpose is taken for granted.…”

Section: Discussionmentioning

confidence: 99%

Decentralized clinical trials: scientific considerations through the lens of the estimand framework

Izem

Zuber

Daizadeh

et al. 2023

Preprint

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While the industry and regulators’ interest in decentralized clinical trials (DCTs) is long-standing, the Covid-19 pandemic accelerated and broadened the adoption and experience with these trials. The key idea in decentralization is bringing the clinical trial design, typically on-site, closer to the patient’s experience (on-site or off-site). Thus, potential benefits of DCTs include reducing the burden of participation in trials, broadening access to a more diverse population, or using innovative endpoints collected off-site. This paper helps researchers to carefully evaluate the added value and the implications of DCTs beyond the operational aspects of their implementation. The proposed approach is to use the ICH E9(R1) estimand framework to guide the strategic decisions around each decentralization component. Furthermore, the framework can guide the process for clinical trialists to systematically consider the implications of decentralization, in turn, for each attribute of the estimand. We illustrate the use of this approach with a fully DCT case study and show that the proposed systematic process can uncover the scientific opportunities, assumptions and potential risks associated with a possible use of decentralization components in the design of a trial. This process can also highlight the benefits of specifying estimand attributes in a granular way. Thus, we demonstrate that bringing a decentralization component into the design will not only impact estimators and estimation but can also correspond to addressing more granular questions, thereby uncovering new target estimands.

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“…[18] Existing commercial and academic tools for electronic data capture were built primarily for research teams, are not designed to meet the preferences of participants interacting with them. [19] Therefore, it is important to create digital tools with engaging functionality for diverse participants that also meet the evolving needs of longitudinal cohort studies. We aimed to build such a platform to support nationwide enrollment of 1-million diverse participants in a novel multiyear, longitudinal cohort study.…”

Section: Introductionmentioning

confidence: 99%

Building a digital health research platform to enable recruitment, enrollment, data collection and follow-up for a highly diverse longitudinal US cohort of 1 million people: The All of Us Research Program (Preprint)

Begale,

Klein,

Klein

et al. 2024

Preprint

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BACKGROUND Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used. OBJECTIVE The National Institutes of Health’s (NIH) All of Us Research Program (AOU) is an ongoing national, multiyear study aimed at building a large research cohort that reflects the diversity of the United States, including minority, health disparate, and other populations underrepresented in biomedical research (UBR). We sought to design and build a highly secure, privacy-preserving, validated, participant-centric digital research platform to recruit, enroll, and engage AOU participants from diverse backgrounds. METHODS AOU applied digital research methods to facilitate multi-site, hybrid, and remote study participation and multimodal data collection. We collaborated with community members, healthcare provider organizations, and NIH leadership to design, build, and validate a secure, feature-rich digital research platform based upon the core values of AOU. Participants were recruited by many methods, including in-person, print, and online digital campaigns. Participants accessed a secure digital research platform via web and mobile applications, either independently or with research staff support. The participant-facing tool facilitated electronic consent, multi-source data collection, including surveys, genomic results, wearables, electronic health records, and ongoing participant engagement. We also built tools for study staff and researchers to provide remote participant support, study workflow management, participant tracking, data analytics, data harmonization, and data management tools. RESULTS We built a secure, participant-centric digital research platform with engaging functionality used to recruit, engage, and collect data from diverse participants throughout the United States. As of April 2024, 87% of participants enrolled via the platform are from UBR groups, including racial and ethnic minorities (46%), rural dwelling individuals (8%), those over the age of 65 (31%), and individuals with low socioeconomic status (20%). CONCLUSIONS This digital research platform demonstrated successful use among diverse participants. We built a user-friendly, participant-centric digital platform with tools to enable engagement with individuals from different racial, ethnic, socioeconomic, and other UBR groups. These findings could be used as best practices for effective use of digital platforms to build and sustain cohorts of various study designs to increase engagement with diverse populations in health research. CLINICALTRIAL N/A

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Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective

Cited by 12 publications

References 23 publications

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Decentralized clinical trials: scientific considerations through the lens of the estimand framework

Building a digital health research platform to enable recruitment, enrollment, data collection and follow-up for a highly diverse longitudinal US cohort of 1 million people: The All of Us Research Program (Preprint)

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