Survival Benefit Associated With Participation in Clinical Trials of Anticancer Drugs
Bibliographic record
Abstract
Importance: Many cancer clinical investigators view clinical trials as offering better care for patients than routine clinical care. However, definitive evidence of clinical benefit from trial participation (hereafter referred to as the participation effect) has yet to emerge. Objective: To conduct a systematic review and meta-analysis of the evidence examining whether patient participation in cancer trials was associated with greater survival benefit compared with routine care. Data Sources: Studies were found through PubMed and Embase (January 1, 2000, until August 31, 2022), as well as backward and forward citation searching. Study Selection: Studies were included that compared overall survival of trial participants and routine care patients. Data Extraction and Synthesis: Data extraction and methodological quality assessment were completed by 2 independent coders using Covidence software. Data were pooled using a random-effects model and analyzed based on the quality of the comparison between trial participants and routine care patients (ie, extent to which studies controlled for bias and confounders). Main Outcomes and Measures: The hazard ratio (HR) for overall survival of trial participants vs routine care patients. Results: Thirty-nine publications were included, comprising 85 comparisons of trial participants and routine care patients. The meta-analysis revealed a statistically significant overall survival benefit for trial participants (HR, 0.76 [95% CI, 0.69-0.82]) when all studies were pooled, regardless of design or quality. However, survival benefits diminished in study subsets that matched trial participants and routine care patients for eligibility criteria (HR, 0.85 [95% CI, 0.75-0.97]) and disappeared when only high-quality studies were pooled (HR, 0.91 [95% CI, 0.80-1.05]). They also disappeared when estimates were adjusted for potential publication bias (HR, 0.94 [95% CI, 0.86-1.03]). Conclusions and Relevance: Many studies suggest a survival benefit for cancer trial participants. However, these benefits were not detected in studies using designs addressing important sources of bias and confounding. Pooled results of high-quality studies are not consistent with a beneficial effect of trial participation on its own.
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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 | 0.131 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".