Evolution of the Randomized Controlled Trial in Oncology Over Three Decades
Bibliographic record
Abstract
PURPOSE: The randomized controlled trial (RCT) is the gold standard for establishing new therapies in clinical oncology. Here we document changes with time in design, sponsorship, and outcomes of oncology RCTs. METHODS: Reports of RCTs evaluating systemic therapy for breast, colorectal (CRC), and non-small-cell lung cancer (NSCLC) published 1975 to 2004 in six major journals were reviewed. Two authors abstracted data regarding trial design, results, and conclusions. Conclusions of authors were graded using a 7-point Likert scale. For each study the effect size for the primary end point was converted to a summary measure. RESULTS: A total of 321 eligible RCTs were included (48% breast, 24% CRC, 28% NSCLC). Over time, the number and size of RCTs increased considerably. For-profit/mixed sponsorship increased substantially during the study period (4% to 57%; P < .001). There was increasing use of time-to-event measures (39% to 78%) and decreasing use of response rate (54% to 14%) as primary end point (P < .001). Effect size remained stable over the study period. Authors have become more likely to strongly endorse the experimental arm (P = .017). A significant P value for the primary end point and industry sponsorship were each independently associated with endorsement of the experimental agent (odds ratio [OR] = 19.6, 95% CI, 8.9 to 43.1, and OR = 3.5, 95% CI, 1.6 to 7.5, respectively). CONCLUSION: RCTs in oncology have become larger and are more likely to be sponsored by industry. Authors of modern RCTs are more likely to strongly endorse novel therapies. For-profit sponsorship and statistically significant results are independently associated with endorsement of the experimental arm.
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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 | 0.625 | 0.721 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.006 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.003 | 0.044 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.027 | 0.025 |
| 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".