Considerations in Launching an International Multisite Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Adequately powered randomized controlled clinical trials (RCTs) provide the highest level of evidence to support nursing interventions that are thought to improve clinical outcomes. To ensure adequate power, recruitment at multiple study sites is often required. Complexity is exponentiated when RCTs are conducted in multiple settings, especially on an international level. Regardless of logistical challenges, funders typically expect subjects to be enrolled within the first year of funding. OBJECTIVE: To describe essential elements that must be considered when launching a multisite RCT. METHODS: Step-by-step review of essential multisite RCT start-up activities using PROSpect , a nurse-led 50+ site international pediatric critical care RCT, as an exemplar. Essential pre- and postaward activities and organizational structure are discussed. The PROSpect launch timeline, with comparisons between United States and international study site initiation, is presented. RESULTS: In the first 2 years of PROSpect funding (June 15, 2018-June 14, 2020), 54 sites in 13 countries spanning five continents initiated study launch activities and subsequently went on to begin subject screening. Of these, 15 sites (28%) began screening within the first funding year and 29 (54%) within the second year. PROSpect enrolled its first subject prior to the end of the first year of funding. Sites completed launch activities within a median of 295 days of receiving an instructional e-mail delineating study launch steps (IQR: 202, 379; range = 151-1,046). International sites ( n = 19) required more time to launch than U.S. sites ( n = 35; 448 [360, 785] vs. 219 [201, 299] days; p < .001). DISCUSSION: Nurses must provide care that is supported by the best available evidence. Multisite RCTs are complex but provide high-quality evidence and can be managed with prospective systematic organization. In PROSpect, international sites took approximately twice as long to activate as U.S. sites. When developing a study start-up timeline, it is important to adjust milestones to accommodate the complexities of launching international sites and maintain momentum throughout the process, as start-up delays can increase the risk of trial failure. This paper contributes to recent efforts to demystify and streamline the multisite RCT start-up process for nurse investigators transitioning from conducting single-site to multisite trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.670 | 0.646 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".