Comparison of randomized controlled trials discontinued or revised for poor recruitment and completed trials with the same research question: a matched qualitative study
Bibliographic record
Abstract
BACKGROUND: More than a quarter of randomized controlled trials (RCTs) are prematurely discontinued, mostly due to poor recruitment of patients. In this study, we systematically compared RCTs discontinued or revised for poor recruitment and completed RCTs with the same underlying research question to better understand the causes of poor recruitment, particularly related to methodological aspects and context-specific study settings. METHODS: We compared RCTs that were discontinued or revised for poor recruitment to RCTs that were completed as planned, matching in terms of population and intervention. Based on an existing sample of RCTs discontinued or revised due to poor recruitment, we identified matching RCTs through a literature search for systematic reviews that cited the discontinued or revised RCT and matching completed RCTs without poor recruitment. Based on extracted data, we explored differences in the design, conduct, and study settings between RCTs with and without poor recruitment, separately for each research question using semi-structured discussions. RESULTS: We identified 15 separate research questions with a total of 29 RCTs discontinued or revised for poor recruitment and 48 RCTs completed as planned. Prominent research areas in the sample were cancer and acute care. The mean number of RCTs with poor recruitment per research question was 1.9 ranging from 1 to 4 suggesting clusters of research questions or settings prone to recruitment problems. The reporting quality of the recruitment process in RCT publications was generally low. We found that RCTs with poor recruitment often had narrower eligibility criteria, were investigator- rather than industry-sponsored, were associated with a higher burden for patients and recruiters, sometimes used outdated control interventions, and were often launched later in time than RCTs without poor recruitment compromising uncertainty about tested interventions through emerging evidence. Whether a multi- or single-center setting was advantageous for patient recruitment seemed to depend on the research context. CONCLUSIONS: Our study confirmed previously identified causes for poor recruitment, i.e., narrow eligibility criteria, investigator sponsorship, and a reduced motivation of patients and recruiters. Newly identified aspects were that researchers need to be aware of all other RCTs on a research question so that compromising effects on the recruitment can be minimized and that a larger number of centers is not always advantageous.
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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 | 0.506 | 0.760 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".