Browsing by Subject "Data Collection"
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Item Centralizing prescreening data collection to inform data-driven approaches to clinical trial recruitment(BioMed Central Ltd., 2023-05-03) Kirn, Dylan R.; Grill, Joshua D.; Aisen, Paul; Ernstrom, Karin; Gale, Seth; Heidebrink, Judith; Jicha, Gregory; Jimenez-Maggiora, Gustavo; Johnson, Leigh A.; Peskind, Elaine; McCann, Kelly; Shaffer, Elizabeth; Sultzer, David; Wang, Shunran; Sperling, Reisa; Raman, RemaBACKGROUND: Recruiting to multi-site trials is challenging, particularly when striving to ensure the randomized sample is demographically representative of the larger disease-suffering population. While previous studies have reported disparities by race and ethnicity in enrollment and randomization, they have not typically investigated whether disparities exist in the recruitment process prior to consent. To identify participants most likely to be eligible for a trial, study sites frequently include a prescreening process, generally conducted by telephone, to conserve resources. Collection and analysis of such prescreening data across sites could provide valuable information to improve understanding of recruitment intervention effectiveness, including whether traditionally underrepresented participants are lost prior to screening. METHODS: We developed an infrastructure within the National Institute on Aging (NIA) Alzheimer's Clinical Trials Consortium (ACTC) to centrally collect a subset of prescreening variables. Prior to study-wide implementation in the AHEAD 3-45 study (NCT NCT04468659), an ongoing ACTC trial recruiting older cognitively unimpaired participants, we completed a vanguard phase with seven study sites. Variables collected included age, self-reported sex, self-reported race, self-reported ethnicity, self-reported education, self-reported occupation, zip code, recruitment source, prescreening eligibility status, reason for prescreen ineligibility, and the AHEAD 3-45 participant ID for those who continued to an in-person screening visit after study enrollment. RESULTS: Each of the sites was able to submit prescreening data. Vanguard sites provided prescreening data on a total of 1029 participants. The total number of prescreened participants varied widely among sites (range 3-611), with the differences driven mainly by the time to receive site approval for the main study. Key learnings instructed design/informatic/procedural changes prior to study-wide launch. CONCLUSION: Centralized capture of prescreening data in multi-site clinical trials is feasible. Identifying and quantifying the impact of central and site recruitment activities, prior to participants signing consent, has the potential to identify and address selection bias, instruct resource use, contribute to effective trial design, and accelerate trial enrollment timelines.Item Quality Assurance Training: Will a New Training Intervention Improve Data Collection of the Texas Emergency Medicine Research Associate Program (TEMRAP)?(2018-12) Saldana, Miguel Antonio; Hodge, Lisa; Pierce, Ava; Krishnamoorthy, RaghuIntroduction: Data collection is vital for the success of a clinical research project. The purpose of this practicum was to address the inadequate data collection by the Texas Emergency Medicine Research Associate Program (TEMRAP) research associates (RAs). The primary goal was to incorporate a more efficient training method to reduce the RAs' error rate in the documentation. The secondary aim of this experiment was to determine if RAs' knowledge of clinical research studies and/or their self-confidence when enrolling a patient had an effect on quality of data collection and if these variables could be improved by a new training method. Methods: A randomized clinical trial was used to evaluate the efficacy of simulated clinical research enrollment training as a teaching and/or learning method to reduce the error rate in submitted research packets by RAs. The returning RAs were randomized into an intervention group with new training (simulations) and a control group with current training (didactic presentations). A self-confidence survey and a knowledge questionnaire were completed by RAs pre/post-training and one-month follow-up. Quality of data collection was measured by comparing the error rates of data collection in completed clinical research enrollment packets submitted by the RAs in the intervention group versus the control group. Results: Results showed no statistically significant difference in the level of knowledge, confidence or error rates between the patient enrollment simulation (intervention) group and the didactic presentations (control) group after their respective training (p [greater than] .05). However, there was a statistically significant increase in knowledge and confidence post-training in patient simulations group. A significant association was present between confidence and error rate but not between knowledge and error rate for research associates in either training group. Conclusion: Clinical simulation training was not a significantly more effecting training method compared to current TEMRAP didactic presentation training. Even though knowledge and confidence did increase post-training there was no significant difference between the two types of training. Future experiments should explore the possibility of combining the two types of training and observing other potential variables affecting the quality of data, such as research associates' motivation. Additionally, the need for a larger sample size and enrolling participants with no prior research experience should be explored for significant results.Item The Future of Medicine: Expoloring the Acceleration of Drug Development with Decentralized Clinical Trials(2023-12) Ajape, Opemipo; Zascavage, Roxanne R.The present study explores the transformative potential of Decentralized Clinical Trials (DCT) within the field of medical research. The primary objective is to investigate how the adoption of DCT influences efficiency, patient engagement, and data quality in medical research. DCT offers the potential to streamline data collection, reduce administrative complexities, and overcome geographical constraints. This is achieved through real-time monitoring, electronic data capture, and remote data gathering techniques, making it particularly advantageous in complex scenarios like rare disease research. These efficiencies can expedite drug development, ensuring timely access to novel treatments; however, concerns about data security and privacy must be addressed to sustain these benefits. Patient engagement is a significant advantage of DCT. It allows patients to participate in research remotely, removing geographical barriers and promoting inclusion. The patient-centric approach in DCT design enhances diversity, recruitment, and retention rates. Nonetheless, maintaining patient engagement may be challenging due to limited face-to-face interactions with healthcare professionals. Data quality in DCT depends on methodologies that generate substantial data, but this complexity requires advanced analytics. DCT emphasizes a practical approach to clinical research, prioritizing patient care and valuable data collection. Ensuring the exclusion of ineligible or fraudulent participants, especially in cases involving financial incentives, is crucial. The implementation of Decentralized Clinical Trials holds the promise of fundamentally transforming medical research by enhancing efficiency, inclusivity, and data-centricity in trials. While challenges exist, current research suggests that the advantages of DCT outweigh the obstacles, paving the way for advancements in medical practices.