CLINICAL TRIALS DATA MANAGEMENT AND TRIAL CONDUCT Clinical Trials 2013; 0: 1–9 Data collection in cancer clinical trials: Too much of a good thing?
Bibliographic record
Abstract
Background Substantial staff time and costs are incurred in the collection of data for cancer clinical trials. Anecdotal experience suggests that much of these data are never used in the analysis or reporting of a trial. Purpose To quantify data items collected in cancer clinical trials and calculate what percentage is used in subsequent published manuscripts. Methods Cancer clinical trials completed by the Ontario Clinical Oncology Group (OCOG) between 2003 and 2012 and the corresponding primary outcome publica-tion were identified. The number of data items collected on each trial’s case report form (CRF) was counted and sorted into 18 categories including eligibility, baseline characteristics, medical history, toxicity, and recurrence. The data items were then counted within the corresponding published manuscripts to determine percent of data used overall and within each section. Results In all, 8 trials, with 9 corresponding publications, were evaluated. The CRF analysis revealed that the total collected items per subject ranged from 186 to 1035 per trial with a median of 599. Across all the publications, a median of 96 data items (18%) were reported in each manuscript, ranging from 11 % to 27 % per trial. In 8 of the 18 categories, 4 % or less of collected data items were used. Limitations The number of trials reviewed is small and were conducted from a sin-gle clinical trial coordinating centre. The main outcome of the number of data items used in the published manuscript is a surrogate for trial information considered valu-able by investigators. Some data may be deemed important by investigators but not included in manuscripts. Conclusions In this analysis of publications from 8 clinical trials, a small amount of data collected was ultimately used in peer-reviewed journal manuscripts. A large amount of data collected in cancer trials appears to go unused and could be omitted from CRFs, thus simplifying data collection and improving trial efficiency. Clinical
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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 | 0.598 | 0.843 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.025 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.037 | 0.027 |
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".