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Using e-technologies in clinical trials.

ABSTRACT: Clinical trials have been slow to incorporate e-technology (digital and electronic technology that utilizes mobile devices or the Internet) into the design and execution of studies. In the meantime, individuals and corporations are relying more on electronic platforms and most have incorporated such technology into their daily lives. This paper provides a general overview of the use of e-technologies in clinical trials research, specifically within the last decade, marked by rapid growth of mobile and Internet-based tools. Benefits of and challenges to the use of e-technologies in data collection, recruitment and retention, delivery of interventions, and dissemination are provided, as well as a description of the current status of regulatory oversight of e-technologies in clinical trials research. As an example of ways in which e-technologies can be used for intervention delivery, a summary of e-technologies for treatment of substance use disorders is presented. Using e-technologies to design and implement clinical trials has the potential to reach a wide audience, making trials more efficient while also reducing costs; however, researchers should be cautious when adopting these tools given the many challenges in using new technologies, as well as threats to participant privacy/confidentiality. Challenges of using e-technologies can be overcome with careful planning, useful partnerships, and forethought. The role of web- and smartphone-based applications is expanding, and the increasing use of those platforms by scientists and the public alike make them tools that cannot be ignored.

SUBMITTER: Rosa C

PROVIDER: S-EPMC4648297 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Using e-technologies in clinical trials.

Rosa Carmen C Campbell Aimee N C AN Miele Gloria M GM Brunner Meg M Winstanley Erin L EL

Contemporary clinical trials 20150712 Pt A

Clinical trials have been slow to incorporate e-technology (digital and electronic technology that utilizes mobile devices or the Internet) into the design and execution of studies. In the meantime, individuals and corporations are relying more on electronic platforms and most have incorporated such technology into their daily lives. This paper provides a general overview of the use of e-technologies in clinical trials research, specifically within the last decade, marked by rapid growth of mobi ...[more]

PMID: 26176884

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Project description:BackgroundDigital health technologies (DHTs) can facilitate the execution of de-centralized trials that can offer opportunities to reduce the burden on participants, collect outcome data in a real-world setting, and potentially make trial populations more diverse and inclusive. However, DHTs can also be a significant source of electronic waste (e-waste). In recognition of the potential health and environmental impact from DHT use in trials, private and public institutions have recently launched initiatives to help measure and manage this e-waste. But in order to develop sound e-waste management policies, it will be necessary to first estimate the current volume of e-waste that results from the use of DHTs in trials.Materials and methodsA Web Ontology Language (OWL)-compliant ontology of DHTs was created using a list of 500 DHT device names derived from a mixture of public and private sources. The U.S. clinical trials registry, ClinicalTrials.gov, was then queried to identify and classify trials using any of the devices in the ontology. The ClinicalTrials.gov records from this search were then analyzed to characterize the volume and properties of trials using DHTs, as well as estimating the total volume of individual DHT units that have been provisioned (or are planned to be provisioned) for clinical research.ResultsOur ontology-driven search identified 2326 unique clinical trials with a reported "actual" enrollment of 200,947 participants and a "planned" enrollment of an additional 4,094,748 participants. The most-used class of DHTs in our ontology was "wearables," (1852 trials), largely driven by the use of smart watches and other wrist-worn sensors (estimated to involve 149,391 provisioned devices). The most-used subtype of DHTs in trials was "subcutaneous" devices (367 trials), driven by the prevalent use and testing of glucose monitors (estimated to involve 17,666 provisioned devices).ConclusionThousands of trials, involving hundreds of thousands of devices, have already been completed, and many more trials (potentially involving millions more devices) are planned. Despite the great opportunities that are afforded by DHTs to the clinical trial enterprise, if the industry lacks the ability to track DHT use with sufficient resolution, the result is likely to be a great deal of e-waste. A new ontology of DHTs, combined with rigorous data science methods like those described in this paper, can be used to provide better information across the industry, and in turn, help create a more sustainable and equitable clinical trials enterprise.

Project description:Advances in genomic technologies and an increased understanding of the molecular pathogenesis of cancer have resulted in development of new effective, mutation-targeted therapies. In turn, these informed the development of Master Trial designs to test these therapies. The Beat Acute Myeloid Leukemia (BAML) Master Trial (Sponsor: The Leukemia & Lymphoma Society) tests several targeted therapies in patients aged ≥ 60 years with AML based on genomic profiling obtained within 7 days of study enrollment. We hypothesized that integrating operational strategies with new electronic technologies (e-technologies) might streamline the conduct and management of this Master Trial. BAML's 5 core operational strategies revolve around the guiding principle of "patients first." The e-technology platforms employed in BAML include: Clinical Oversight Platform: a central collaborative tool; e-Protocol/e-Source Upload/Electronic Data Capture Platform: digitizes the protocol, allows remote data monitoring, and collects/exports data in Study Data Tabulation Model format; and Data Review Platform: ingests data from different sources for clinical response and safety data reviews. The operational approaches, e-technologies and sponsor/contract research organization's (CRO) expertise together allow: the complexity and size of the BAML Master Trial to be better managed; near real-time study data oversight; better collaboration, communication and training; improved data collection, enhanced transmission and accessibility; data integration, review and generation of reports; while maintaining data privacy, and compliance. Initial e-technology challenges were overcome through training, learning, discipline and adjustment. In conclusion, to successfully manage Master Trials, significant time should be spent re-evaluating, improving and developing new operational approaches.Clinical Trial Registration: Clinical Trials.gov Identifier: NCT03013998. https://clinicaltrials.gov/ct2/show/NCT03013998 .

Project description:BackgroundRandomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.MethodsWe survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.ResultsRestricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.ConclusionsThe choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

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Using e-technologies in clinical trials.

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Using e-technologies in clinical trials.

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