Premature Discontinuation of Prospective Clinical Studies Approved by a Research Ethics Committee – A Comparison of Randomised and Non-Randomised Studies
Bibliographic record
Abstract
BACKGROUND: Premature discontinuation of clinical studies affects about 25% of randomised controlled trials (RCTs) which raises concerns about waste of scarce resources for research. The risk of discontinuation of non-randomised prospective studies (NPSs) is yet unclear. OBJECTIVES: To compare the proportion of discontinued studies between NPSs and RCTs that received ethical approval. METHODS: We systematically surveyed prospective longitudinal clinical studies that were approved by a single REC in Freiburg, Germany between 2000 and 2002. We collected study characteristics, identified subsequent publications, and surveyed investigators to elucidate whether a study was discontinued and, if so, why. RESULTS: Of 917 approved studies, 547 were prospective longitudinal studies (306 RCTs and 241 NPSs). NPSs were on average smaller than RCTs, more frequently single centre and pilot studies, and less frequently funded by industry. NPSs were less frequently discontinued than RCTs: 32/221 (14%) versus 78/288 (27%, p<0.001, missing data excluded). Poor recruitment was the most frequent reason for discontinuation in both NPSs (36%) and RCTs (37%). CONCLUSIONS: Compared to RCTs, NPSs were at lower risk for discontinuation. Measures to reliably predict, sustain, and stimulate recruitment could prevent discontinuation of many RCTs but also of some NPSs.
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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 | 0.748 | 0.849 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".