Early closure of clinical trials: The experience of the National Cancer Institute of Canada Clinical Trials Group
Bibliographic record
Abstract
6053 Background: Phase III studies require a significant commitment on behalf of researchers and patients. Closure of a study before the originally planned number of patients have been enrolled may be due to a number of reasons such as poor accrual, information within the study that precludes continuation such as excess toxicity, an interim futility or extreme efficacy analysis or data from outside sources that render the study question obsolete. Methods: We reviewed the phase III activity of our group since inception. Reasons for early closure were classified in the following manner: accrual failure (AF), external information (EI), internal information (II). Studies were grouped by site and time period of study activation to demonstrate any trends over time. Results: 94 phase III studies led by our group were identified from our roster. Reasons for early closure are presented below. Other sites include brain with an early closure due to AF, head/neck where 1 of 3 studies closed due to AF, melanoma where 1 of 3 studies closed due to EI and sarcoma where 2 studies were successfully completed. Several of the studies that closed for accrual failure were nevertheless published either singly or as part of a meta-analysis. Conclusions: Slightly over one third of studies closed prior to achievement of the targeted sample size. Accrual failure continues to be the main cause of early study closure (27/34 or 80%) with a trend towards decreasing frequency of occurrence over time. Emerging data within or external to a study leading to study closure are important but relatively rare reasons for early closure. [Table: see text] No significant financial relationships to disclose.
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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 | 0.296 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".