Recruitment of participants to a multiple sclerosis trial: The CombiRx experience
Bibliographic record
Abstract
BACKGROUND: and purpose Participant recruitment is central to all clinical trials. Any delay in recruitment affects the completion and ultimate success of the trial. We report our experience with patient screening and randomization in CombiRx, which may inform the design of other trials. CombiRx was a multicenter, phase III, double-blind, randomized clinical trial comparing the combined use of interferon beta-1a and glatiramer acetate to either agent alone in patients with relapsing-remitting multiple sclerosis (RRMS). This trial was launched in January 2005 in 69 centers in the United States and Canada under a co-operative agreement with the National Institute of Neurological Disorders and Stroke (NINDS). The goal was to recruit 1000 patients over 1.5 years after a 6-month start-up period. Instead, the investigators required 4.25 years to enroll 1008 patients. METHODS: During this trial, we assessed the effectiveness of various recruitment strategies, utility of rescreening prior screen failures, and potential factors and strategies used in study conduct, research, and infrastructure, all of which affected recruitment of participants and ultimately time to completion of CombiRx. We particularly were interested in the variability in time to site initiation between academic centers and private practice sites. RESULTS: Physicians who were directly involved in the medical care of patients with RRMS were the primary source of patients recruited to CombiRx. A flexible study design that allowed for rescreening of the initial screen failures after a period of time was useful due to the relapsing/remitting course of the disease. Academic centers took longer to implement the trial than the private practice centers, but once sites were approved for enrollment, there was no important difference in the number of participants enrolled. LIMITATIONS: The CombiRx trial was conducted during a period when multiple new medications were being tested, thus affecting the pace of recruitment and limiting ability to generalize our experiences. However, the lessons we learned about process are relevant. CONCLUSION: Participants can be enrolled successfully in a clinical trial for RRMS, but factors affecting the time to achieve the requirements needed to start screening can be unpredictable and problematic. Prospective planning by the sponsors and investigators, use of central institutional review boards (IRBs), master trial agreements and secure remote desktop access to the trial database may expedite trial implementation and participant recruitment. A good scientific research question with flexible study design and active involvement of the clinicians are important factors driving recruitment. Clinical trials can be implemented successfully both in private practices and at academic centers, a consideration when selecting sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.117 | 0.774 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".