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Decentralized Clinical Trials: A New Paradigm for New Medical Product Development and Digital Therapeutics
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Isaac R. Rodriguez-Chavez, Greg Licholai
Operational improvements through DCTs suggest benefits to investigators, sites, sponsors, and other parties involved in these investigations (e.g., vendors, payers, and depots for IMP distribution). DCTs may be aligned with local healthcare infrastructures and use local providers and clinical services such as clinical laboratories, pharmacies, and imaging services to reduce the burden on participants, caregivers, and sites. Expedited recruitment through multipronged approaches (including mobile communication platforms) leads to increased access to and improved retention of participants, which is considered a significant benefit of DCTs. Opportunities for home administration of IMPs may be more representative of real-world administration and post-approval usage. The incorporation of reminders suggests that “real-world” data can be more easily incorporated into clinical research, improving treatment populations' effectiveness. The logistics of decentralization means shifting towards using local HCPs, clinical diagnostic laboratories, medical imaging services, local pharmacies, and increasing involvement of local allied health professionals to reduce the burden on traditional clinical research sites. The ultimate benefit is that DCTs may lead to faster and less expensive trials in the long term for sponsors. More rapid trial participant recruitment may accelerate trial participant access to important medical interventions and reduce long-term costs for sponsors.
Health Information Technology
Published in Kelly H. Zou, Lobna A. Salem, Amrit Ray, Real-World Evidence in a Patient-Centric Digital Era, 2023
Joseph P. Cook, Gabriel Jipa, Claudia Zavala, Lobna A. Salem
The process of selecting a technology should start with the business research questions or unmet healthcare needs—known or unknown. Statistical methods in the exploratory data analysis applied to big data are still relevant but require specific tools (Scott, 2018), as this will generate new questions or assess the data quality for its variables, allowing researchers to propose the addition or exclusion of categorical variables or to propose common factor analysis, based on domain knowledge. Various methods, such as bias, outlier detection, multiple collinearity, and visualization can be used to understand the structure and quality of the data. The decision of data integration, harmonization in common formats should follow the research question needs. Beside the data analysis, in some cases qualitative research methods can be used to understand the specific of the collected datasets and uncover unmet needs. Real-world Data usage might raise ethical and privacy concerns and concerns on the initial nature of data collection (Lipworth, 2019).
Off-label use of medicines between clinical research and practice
Published in Andrea Parziale, The Law of Off-label Uses of Medicines, 2023
Unlike clinical trials, non-interventional studies do not determine the treatment. Instead, they observe how a product is used in actual medical practice, with no interference in their course. This is why they are also designated as observational studies. Typically, non-interventional studies monitor clinical practice based on real-world data collected from the provider’s notes (e.g., patient registries).69 Directive 2001/20 reflects this definition. It states that in non-interventional studies the medicinal product is prescribed in the usual manner following the terms of the marketing authorisation. The assignment of the patient to a particular therapeutic strategy is not decided in advance by a trial protocol but falls within current practice, and the prescription of the medicine is separated from the decision to enrol the patient in the investigation. Non-interventional studies do not apply additional diagnostic or monitoring procedures to patients and use epidemiological methods for the analysis of collected data.70
Identification of cancer chemotherapy regimens and patient cohorts in administrative claims: challenges, opportunities, and a proposed algorithm
Published in Journal of Medical Economics, 2023
Catherine M. Lockhart, Cara L. McDermott, Aaron B. Mendelsohn, James Marshall, Ali McBride, Gary Yee, Minghui Sam Li, Aziza Jamal-Allial, Djeneba Audrey Djibo, Gabriela Vazquez Benitez, Terese A. DeFor, Pamala A. Pawloski
Observational research and real-world evidence (RWE) are valuable sources of information for stakeholders and decision-makers in the healthcare system, including clinicians, patients, payers, and regulatory authorities1,2. In 2016, the twenty first Century Cures Act amplified interest in RWE through a provision recommending that the United States Food and Drug Administration (FDA) to identify ways to incorporate it into regulatory decisions3. Typical sources of real-world data used to generate RWE include electronic health records, insurance claims, disease registries, and patient-generated data2. These sources are rich with data generated each time an individual interacts with the healthcare system, including clinic visits and prescription claims; however, these data are typically collected for reasons other than research (e.g. recording a clinical encounter or submitting claims for reimbursement) so there are challenges in adapting them into the context of real-world research4.
An overview of methodological flaws of real-world studies investigating drug safety in the post-marketing setting
Published in Expert Opinion on Drug Safety, 2023
Salvatore Crisafulli, Zakir Khan, Yusuf Karatas, Marco Tuccori, Gianluca Trifirò
Real-world data (RWD) is defined by the Food and Drug Administration (FDA) as ‘the data regarding patient health status and/or the delivery of health care routinely obtained from a range of sources. Examples of RWD include data derived from electronic health records, medical claims data, data from product or disease registries, and data gathered from other sources (such as digital health technologies) that can inform on health status.’ These data can be derived from a variety of sources, including electronic health records (EHRs), drug/disease registries, claims databases, patient-generated data including data collected in home-use settings and collected from other sources (e.g. wearable devices), although only few demonstrations of evidence derived from wearable devices exist [11,12]. Real-world evidence (RWE) is defined as the clinical evidence derived from the analysis of RWD that addresses the use of medical products and their potential benefits or risks. RWE can be generated by a variety of study designs or analyses, including prospective and/or retrospective observational studies, randomized trials, pragmatic, and large simple trials [11].
Abiraterone acetate versus docetaxel for metastatic castration-resistant prostate cancer: a cohort study within the French nationwide claims database
Published in Expert Review of Clinical Pharmacology, 2022
Nicolas H. Thurin, Magali Rouyer, Jérémy Jové, Marine Gross-Goupil, Thibaud Haaser, Xavier Rébillard, Michel Soulié, Gérard de Pouvourville, Camille Capone, Marie-Laure Bazil, Fatiha Messaoudi, Stéphanie Lamarque, Emmanuelle Bignon, Cécile Droz-Perroteau, Nicholas Moore, Patrick Blin
Real-world data allow a better understanding of clinical practice and can facilitate treatment decision-making for prescribers. A number of observational studies in the real-world setting have been performed with abiraterone acetate in mCRPC. A systematic review of eight such studies performed up to 2018 [18] reported outcomes that were in general less favorable than those reported in the pivotal trial of abiraterone acetate [8]. However, the included studies were heterogeneous in design and the patients included, were generally retrospective and included rather low number of patients (the largest number of patients studied in any of the constituent studies was 204 [19]). Since then, a larger multinational retrospective study [20] and a report from a multinational patient registry [21] have reported similar outcomes. Outcomes in these real-world studies are difficult to compare to those reported in real-world studies of docetaxel (e.g. [22,23]), due to differences in how the studies were conducted and the characteristics of the patients enrolled.