Compliance with results reporting at ClinicalTrials.gov.

BACKGROUND: The Food and Drug Administration Amendments Act (FDAAA) mandates timely reporting of results of applicable clinical trials to ClinicalTrials.gov. We characterized the proportion of applicable clinical trials with publicly available results and determined independent factors associated with the reporting of results. METHODS: Using an algorithm based on input from the National Library of Medicine, we identified trials that were likely to be subject to FDAAA provisions (highly likely applicable clinical trials, or HLACTs) from 2008 through 2013. We determined the proportion of HLACTs that reported results within the 12-month interval mandated by the FDAAA or at any time during the 5-year study period. We used regression models to examine characteristics associated with reporting at 12 months and throughout the 5-year study period. RESULTS: From all the trials at ClinicalTrials.gov, we identified 13,327 HLACTs that were terminated or completed from January 1, 2008, through August 31, 2012. Of these trials, 77.4% were classified as drug trials. A total of 36.9% of the trials were phase 2 studies, and 23.4% were phase 3 studies; 65.6% were funded by industry. Only 13.4% of trials reported summary results within 12 months after trial completion, whereas 38.3% reported results at any time up to September 27, 2013. Timely reporting was independently associated with factors such as FDA oversight, a later trial phase, and industry funding. A sample review suggested that 45% of industry-funded trials were not required to report results, as compared with 6% of trials funded by the National Institutes of Health (NIH) and 9% of trials that were funded by other government or academic institutions. CONCLUSIONS: Despite ethical and legal obligations to disclose findings promptly, most HLACTs did not report results to ClinicalTrials.gov in a timely fashion during the study period. Industry-funded trials adhered to legal obligations more often than did trials funded by the NIH or other government or academic institutions. (Funded by the Clinical Trials Transformation Initiative and the NIH.).

Standardizing, harmonizing, and protecting data collection to broaden the impact of COVID-19 research: the rapid acceleration of diagnostics-underserved populations (RADx-UP) initiative.

OBJECTIVE: The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis. MATERIALS AND METHODS: The RADx-UP consortium began with more than 700 CDEs from the National Institutes of Health (NIH) CDE Repository, Disaster Research Response (DR2) guidelines, and the PHENotypes and eXposures (PhenX) Toolkit. Following a review of initial CDEs, we made selections and further refinements through an iterative process that included live forums, consultations, and surveys completed by the first 69 RADx-UP projects. RESULTS: Following a multistep CDE development process, we decreased the number of CDEs, modified the question types, and changed the CDE wording. Most research projects were willing to collect and share demographic NIH Tier 1 CDEs, with the top exception reason being a lack of CDE applicability to the project. The NIH RADx-UP Tier 1 CDE with the lowest frequency of collection and sharing was sexual orientation. DISCUSSION: We engaged a wide range of projects and solicited bidirectional input to create CDEs. These RADx-UP CDEs could serve as the foundation for a patient-centered informatics architecture allowing the integration of disease-specific databases to support hypothesis-driven clinical research in underserved populations. CONCLUSION: A community-engaged approach using bidirectional feedback can lead to the better development and implementation of CDEs in underserved populations during public health emergencies.

Improving and sustaining the site investigator community: Recommendations from the Clinical Trials Transformation Initiative.

The Clinical Trials Transformation Initiative (CTTI) Strengthening the Investigator Community Project was prompted by the need to understand the reasons for high rates of turnover among investigators who lead US Food and Administration-regulated clinical trials at research sites. Because investigator knowledge and experience directly affect the quality and ultimate success of clinical trials, investigator turnover has important implications for the research enterprise, as well as the patients and other stakeholders who depend on the outcomes of clinical research. The CTTI project team used findings from both quantitative and qualitative research activities, as well as input from an expert meeting with multiple stakeholders, to delineate key concerns faced by investigators and recommend practical, action-based solutions. The recommendations focus on strengthening four key categories of site-based research activity: developing site-based research infrastructure and staff, optimizing trial execution and conduct, improving site budget development and contract negotiations, and discovering opportunities for conducting additional trials.

Trends in clinical trial investigator workforce and turnover: An analysis of the U.S. FDA 1572 BMIS database.

BACKGROUND: High turnover rates among clinical trial investigators contribute to inefficiency, instability, and increased costs for the clinical research enterprise; however, factors contributing to investigator turnover have not been well characterized. METHODS: Using information from the U.S. Food and Drug Administration's Bioresearch Monitoring Information System (BMIS), we examined trends in the overall clinical investigator workforce and within specific "phenotypes" as well as differences by investigator location (U.S.-based vs. non-U.S.-based). We identified unique investigators within the database, stratifying them into one of three "phenotypes": those with one Form FDA1572 submission across the study interval ("one-and-done"); those with two or more submissions but with substantial intervals between trials ("stop-and-go"); and those with two or more submissions and continuous involvement in multiple trials ("stayers"). RESULTS: Of the 172,453 unique investigators who submitted a Form FDA 1572 during the study interval (1999-2015), 85,455 were classified as "one-and-done" investigators; 21,768 as "stop-and-go" investigators; and 65,231 as "stayer" investigators. The total number of investigators declined across the study interval. Among all subgroups, only "one-and-done" investigators showed growth across the study period, largely driven by increases in non-U.S.-based investigators. "Stop-and-go" investigators showed declines for both U.S.-based and non-U.S.-based investigators, as did "stayers," who showed the largest absolute and proportional declines of all subgroups. CONCLUSIONS: From 1999 to 2015, investigators submitting a Form FDA 1572 to the BMIS database declined by approximately one-third and the proportion of investigators involved in only one trial increased, signaling potential adverse trends in the clinical investigator workforce. Strategies for sustaining investigator engagement warrant further exploration.

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®).

INTRODUCTION: Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data. METHODS: PCORnet's Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals. RESULTS: The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners. DISCUSSION: The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.

Effects of exercise training amount on physical activity energy expenditure.

INTRODUCTION: We examined the effects of three exercise training interventions on total physical activity energy expenditure (PAEE) or nonexercise PAEE in a randomized controlled trial where sedentary, overweight, and obese men and women were assigned to inactive control, low-amount/moderate-intensity, low-amount/vigorous-intensity, or high-amount/vigorous-intensity aerobic exercise. METHODS: To measure PAEE, triaxial RT3 accelerometers were worn by subjects for 7 d at the beginning and end of an 8-month exercise intervention. In total, 50 subjects (control, n = 8; two low-amount groups, n = 28; high-amount group, n = 14) had usable PAEE data collected at both time points. RESULTS: At baseline, subjects had an average age of 53.2 yr, had a body mass index of 29.7 kg x m(-2), and a relative peak VO2 of 28.7 mL x kg(-1) x min(-1). There were no significant differences between groups at baseline. After the intervention, average change in total PAEE was 8.4 +/- 20.9 kJ x h(-1) for controls, 58.6 +/- 20.9 kJ x h(-1) for the two low-amount groups, and 138.1 +/- 33.5 kJ x h(-1) for the high-amount group (means +/- SE). The high-amount group experienced a significantly greater increase in total PAEE compared with the controls (P = 0.02). As expected, total PAEE increased with increasing exercise volume. Average change in nonexercise PAEE was 8.4 +/- 20.9 kJ x h(-1) for control, 25.1 +/- 20.9 kJ x h(-1) for the low-amount groups combined, and 62.8 +/- 29.3 kJ x h(-1) for the high-amount group. There was no statistically significant difference in change of nonexercise PAEE among groups. CONCLUSIONS: We conclude that in middle-aged overweight or obese subjects participating in an extended exercise intervention, total PAEE increased, and there was no compensatory decrease in nonexercise PAEE.