ABSTRACT
Technological advances and unique data collection requirements across multiple data streams have led to a proliferation of options for data collection in clinical trials. This includes traditional clinical trial data collected in 21CFR Part 11-compliant electronic casereport forms, patient-reported outcomes via standardized assessments and/or computerized tasks, passive or interactive data collection from application-based systems, provider, and/or hospital/clinic-level surveys. The COVID-19 pandemic has led to additional data collection considerations and necessitated policy changes accelerating the ability to conduct health care, clinical trial study visits, and other data collection procedures remotely. Such developments will leave enduring marks on policy related to healthcare and the conduct of clinical trials. The National Institute on Drug Abuse Clinical Trials Network (NIDA CTN) conducts multisite clinical trials on pharmacological and behavioral interventions for Substance Use Disorders (SUDs). The Data and Statistics Center at Emmes is responsible for developing data systems across many NIDA CTN trials and works with Lead Investigative teams such as the New York Node to develop novel approaches to data collection that suit the needs of individuals with SUDs and the studies designed to test and implement effective treatments. This presentation will include different perspectives from members of these teams. Challenges with technological literacy, access to smartphone devices with active data plans, and limitations to remote support from research staff can be barriers to completing assessments for SUD populations. The development of different data system approaches allowing (1) remote completion of web-based surveys and (2) encrypted, automated remote login to a 21CFR11-compliant system (no user ID and password required) will be discussed, as well as interactions between systems and considerations of approaches for different clinical research scenarios. For example, populating a survey link onto an eCRF if research staff assistance is needed for survey completion or for manual distribution of unique links embedded with participant information. Pros and cons of varied approaches to allow participants to access surveys will be discussed, including QR codes, automated vs personalized distribution via SMS and email, and mailed paper invitations with vanity URLs, which may differ by respondent population. In addition, studies targeting a specific patient population may require prescreening a large number in order to attain enrollment targets (e.g. tens of thousands of primary care patients who are sub-clinical threshold but at-risk for SUD). Approaches for conducting extensive prescreening anonymously and lessons learned (predicting prescreen targets accurately, monitoring enrollment rate, staffing and resource considerations) will be presented. Finally, an increasing number of clinical trials examine implementation of SUD interventions in specific patient populations or settings alongside traditional efficacy outcomes. Implementation assessments present unique considerations for data collection;in particular, the need to collect pre-implementation assessments of provider and site-level readiness prior to the onset of training and the new intervention;and before sites have been endorsed for trial data collection in order to avoid contamination. Another unique consideration involves qualitative data collection around the implementation process, and any adaptations made to the intervention or implementation facilitation based on a combination of qualitative and quantitative data. Balancing implementation needs with efficacy testing in clinical trials will also be discussed. Dr. Kathryn Hefner is a clinical psychologist and the Scientific Director of the National Institute on Drug Abuse Clinical Trials Network's (NIDA CTN) Data and Statistics Center (DSC). Her research interests involve substance use and effective treatments for substance use and comorbid mental health conditions. She leads the DSC's efforts in p tient-reported outcomes, including the adoption of novel data collection practices for the NIDA CTN. In addition, she serves as Co- Lead Investigator on CTN-0126, a longitudinal followup study of participants in CTN-0097 and CTN-0100. Dr. Matisyahu Shulman is a clinician-scientist at New York State Psychiatric Institute (NYSPI) and Columbia University Irving Medical Center. His research focus is on opioid use disorders clinical trials and the use of technology to enhance implementation, quality improvement, and treatment delivery. He is part of the lead team of several large multisite opioid treatment trials, including the CTN-0097 trial, a hybrid type I effectiveness-implementation trial, and CTN- 0126, a longitudinal follow-up study. Ms. Onumara Opara, MPH, is the National Project Director for CTN-0097. She has over 9 years of experience in project management for clinical and community-based research. Onumara oversees the dayto- day operations of the CTN-0097 trial, including coordination of protocol activities, implementation efforts at clinical sites, organization and supervision of study teams, training of research staff, quality assurance, and monitoring study progress. Ms. Christina Scheele,MA, is a Senior Data Manager at the CTN Data and Statistics Center at the Emmes Company. She has supported multiple CTN studies during system development and helped to troubleshoot and implement novel approaches to data collection to ease participant and staff burden. Ms. Scheele also has experience using multiple electronic database capture systems for other research studies which required unique and innovative system development for implementation. She currently serves as Data Management Lead on the CTN- 0097, CTN-0100, and CTN-0126 studies. Rebecca Price is a Senior Data Manager who has been at Emmes since 2016, currently supporting the NIDA DSC. Rebecca's main role has included leading the development, maintenance, and/or closeout activities for multiple CTN studies, and she has played a central role in adopting novel data collection practices across several NIDA CTN studies. Dr. Greiner is an addiction psychiatrist in training and a T32 postdoctoral fellow in the Division of Substance Use Disorders at Columbia University and NYSPI. Her main research interest is in implementation and dissemination of evidence-based interventions for substance use disorders in community settings. In particular, she is interested in implementation strategies, costs associated with implementing and sustaining interventions across different organizations, and developing methodology around implementation process. Dr. Greiner will discuss the hybrid effectiveness-implementation NIDA CTN (CTN-0097) trial led by principal investigators Drs. Adam Bisaga and Edward Nunes.
ABSTRACT
Background: Trial recruitment that requires specific actionable mutations based on next-generation sequencing (NGS) is challenging. Barriers can include competing studies, physician study awareness, site proximity, mutation incidence, among other concerns. Methods: This study (NCT04432454) opened clinical sites using two methods during the COVID-19 pandemic. The “Traditional” approach included site selection, IRB and contract approval, and trial activation prior to a patient being identified for enrollment. The second approach used the Tempus “TIME” Trials network that would only open a site after identifying a patient with a mutation of interest and eligible for the trial. Results: The first patient enrolled was on 10/12/20 and the last patient was on 6/24/21. A total of 16 sites (6 Traditional and 10 TIME) participated. All Traditional sites, and none of the TIME sites, were affiliated with major academic institutions. Duration for full CTA execution for Traditional sites averaged 200.5 days (range 142 to 257) and for TIME sites averaged 7.6 days (range 2 to 14). IRB approval time average for Traditional sites was 27.5 days (range 12 to 71) and TIME sites was 3.0 days (range 1 to 12 days). Days from site selection to activation letter for Traditional sites was on average 250.0 days (range 187 to 281) and for TIME sites was 131.6 days (range 22 to 248). Time from study activation to first consent was 33.3 days (range 18 to 58) for Traditional sites and 8.8 days (range 1 to 35) for TIME sites. The first patient on-study was at a TIME site 115 days prior to a Traditional site and the first 7 patients enrolled were at TIME sites. Traditional sites consented 23 and enrolled 16 patients while the TIME sites consented 16 and enrolled 13. The trial enrolled all 29 patients in 8 months with the anticipated enrollment duration being 12 to 18 months. Conclusions: Although the Traditional and TIME programs had different operational models, they both contributed a significant number of patients and reduced the projected enrollment timeline. TIME sites enrolled the initial patients. These results demonstrate that the “Just-in-Time model,” in conjunction with a Traditional model, can reduce projected overall time to enrollment in biomarker-driven studies.