Exploring reasons for recruitment failure in clinical trials: a qualitative study with clinical trial stakeholders in Switzerland, Germany, and Canada
Bibliographic record
Abstract
BACKGROUND: Poor participant recruitment is the most frequent reason for premature discontinuation of randomized clinical trials (RCTs), particularly if they are investigator-initiated. The aims of this qualitative study were to investigate (1) the views of clinical trial stakeholders from three different countries regarding reasons for recruitment failure in RCTs and (2) how these compare and contrast with the causes identified in a previous systematic review of RCT publications. METHODS: From August 2015 to November 2016, we conducted 49 semi-structured interviews with a purposive sample of clinical trial stakeholders. This included investigators based in Germany (n = 9), Switzerland (n = 6) and Canada (n = 1) with personal experience of a discontinued RCT and 33 other stakeholders (e.g., representatives of ethics committees, clinical trial units, pharmaceutical industry) in Switzerland. Individual semi-structured qualitative interviews were conducted and analyzed using thematic analysis. RESULTS: Interviewees identified a total of 29 different reasons for recruitment failure. Overoptimistic recruitment estimates, too narrow eligibility criteria, lack of engagement of recruiters/trial team, lack of competence/training/experience of recruiters, insufficient initial funding, and high burden for trial participants were mentioned most frequently. The interview findings largely confirm the previous systematic review on published reasons for recruitment failure. However, eight new reasons for recruitment failure were identified in the interviews, which led to the checklist of reasons for recruitment failure being revised and a new category describing research environment-related factors being added. CONCLUSIONS: This study highlights the diversity of often interlinked reasons for recruitment failure in RCTs. Integrating the findings of this interview study with a previous systematic review of RCT publications led to a comprehensive, structured checklist of empirically-informed reasons for recruitment failure. The checklist may be useful to guide further research on interventions to improve participant recruitment in RCTs and helpful for trial investigators, research ethics committees, and funding agencies when assessing trial feasibility with respect to recruitment.
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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 | 0.045 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".