Interim reports, participant closeout, and study archives

Informatics infrastructure in a rural pediatric clinical trials network: Matching specific clinical research needs with best practices and industry guidelines

Children have historically been underrepresented in randomized controlled trials and multi-center studies. This is particularly true for children who reside in rural and underserved areas. Conducting multi-center trials in rural areas presents unique informatics challenges. These challenges call for increased attention towards informatics infrastructure and the need for development and application of sound informatics approaches to the collection, processing, and management of data for clinical studies. By modifying existing local infrastructure and utilizing open source tools, we have been able to successfully deploy a multi-site data coordinating and operations center. We report our implementation decisions for data collection and management for the IDeA States Pediatric Clinical Trial Network (ISPCTN) based on the functionality needed for the ISPCTN, our synthesis of the extant literature in data collection and management methodology, and Good Clinical Data Management Practices.

Early warnings and repayment plans: novel trial management methods for monitoring and managing data return rates in a multi-centre phase III randomised controlled trial with paper Case Report Forms

Background

Monitoring and managing data returns in multi-centre randomised controlled trials is an important aspect of trial management. Maintaining consistently high data return rates has various benefits for trials, including enhancing oversight, improving reliability of central monitoring techniques and helping prepare for database lock and trial analyses. Despite this, there is little evidence to support best practice, and current standard methods may not be optimal.

Methods

We report novel methods from the Trial of Imaging and Schedule in Seminoma Testis (TRISST), a UK-based, multi-centre, phase III trial using paper Case Report Forms to collect data over a 6-year follow-up period for 669 patients. Using an automated database report which summarises the data return rate overall and per centre, we developed a Microsoft Excel-based tool to allow observation of per-centre trends in data return rate over time. The tool allowed us to distinguish between forms that can and cannot be completed retrospectively, to inform understanding of issues at individual centres. We reviewed these statistics at regular trials unit team meetings. We notified centres whose data return rate appeared to be falling, even if they had not yet crossed the pre-defined acceptability threshold of an 80% data return rate. We developed a set method for agreeing targets for gradual improvement with centres having persistent data return problems. We formalised a detailed escalation policy to manage centres who failed to meet agreed targets. We conducted a post-hoc, descriptive analysis of the effectiveness of the new processes.

Results

The new processes were used from April 2015 to September 2016. By May 2016, data return rates were higher than they had been at any time previously, and there were no centres with return rates below 80%, which had never been the case before. In total, 10 centres out of 35 were contacted regarding falling data return rates. Six out of these 10 showed improved rates within 6–8 weeks, and the remainder within 4 months.

Conclusions

Our results constitute preliminary effectiveness evidence for novel methods in monitoring and managing data return rates in randomised controlled trials. We encourage other researchers to work on generating better evidence-based methods in this area, whether through more robust evaluation of our methods or of others.

Data management plans: the missing perspective

The National Institutes of Health requires data sharing plans for projects with over five hundred thousand dollars in direct costs in a single year and has recently released a new guidance on rigor and reproducibility in grant applications. The National Science Foundation outright requires Data Management Plans (DMPs) as part of applications for funding. However, there is no general and definitive list of topics that should be covered in a DMP for a research project. We identified and reviewed DMP requirements from research funders. Forty-three DMP topics were identified. The review uncovered inconsistent requirements for written DMPs as well as high variability in required or suggested DMP topics among funder requirements. DMP requirements were found to emphasize post-publication data sharing rather than upstream activities that impact data quality, provide traceability or support reproducibility. With the emphasis equalized, the forty-three identified topics can aid Data Managers in systematically generating comprehensive DMPs that support research project planning and funding application evaluation as well as data management conduct and post-publication data sharing.

Analysis of professional competencies for the clinical research data management profession: implications for training and professional certification

OBJECTIVE

To assess and refine competencies for the clinical research data management profession.

MATERIALS AND METHODS

Based on prior work developing and maintaining a practice standard and professional certification exam, a survey was administered to a captive group of clinical research data managers to assess professional competencies, types of data managed, types of studies supported, and necessary foundational knowledge.

RESULTS

Respondents confirmed a set of 91 professional competencies. As expected, differences were seen in job tasks between early- to mid-career and mid- to late-career practitioners. Respondents indicated growing variability in types of studies for which they managed data and types of data managed.

DISCUSSION

Respondents adapted favorably to the separate articulation of professional competencies vs foundational knowledge. The increases in the types of data managed and variety of research settings in which data are managed indicate a need for formal education in principles and methods that can be applied to different research contexts (ie, formal degree programs supporting the profession), and stronger links with the informatics scientific discipline, clinical research informatics in particular.

CONCLUSION

The results document the scope of the profession and will serve as a foundation for the next revision of the Certified Clinical Data Manager TM exam. A clear articulation of professional competencies and necessary foundational knowledge could inform the content of graduate degree programs or tracks in areas such as clinical research informatics that will develop the current and future clinical research data management workforce.

Closeout of the HALT-PKD trials

BACKGROUND

The HALT Polycystic Kidney Disease Trials Network consisted of two randomized, double blind, placebo-controlled trials among patients with autosomal dominant polycystic kidney disease. The trials involved 5-8years of participant follow-up with interventions in blood pressure and antihypertensive therapy. We provide a framework for designing and implementing closeout near the end of a trial while ensuring patient safety and maintaining scientific rigor and study morale.

METHODS

We discuss issues and resolutions for determining the last visit, tapering medications, and unblinding of participants to study allocation and results. We also discuss closure of clinical sites and Data Coordinating Center responsibilities to ensure timely release of study results and meeting the requirements of regulatory and funding authorities.

RESULTS

Just over 90% of full participants had a 6-month study visit prior to their last visit preparing them for trial closeout. Nearly all patients wanted notification of study results (99%) and treatment allocation (99%). All participants were safely tapered off study and open label blood pressure medications. Within 6months, the trials were closed, primary papers published, and 805 letters distributed to participants with results and allocation. DCC obligations for data repository and clinicaltrials.gov reporting were completed within 12months of the last study visit.

CONCLUSIONS

Closeout of our trials involved years of planning and significant human and financial resources. We provide questions for investigators to consider when planning closeout of their trials with focus on (1) patient safety, (2) dissemination of study results and (3) compliance with regulatory and funding responsibilities.

Clinical research in neurology. From observation to experimentation

Clinical research in neurology may be based on observation or intervention. Basic science and observational clinical studies often address questions of cause and pathogenesis, whereas interventional clinical experiments normally are required to establish the safety, tolerability, and efficacy of therapies. This article outlines various study paradigms and discusses the concepts of intervention, randomization, blinding, and control in the context of clinical trial design. Accurate interpretation of trial results is predicated on the care with which experimental therapeutic studies are designed and conducted.