Objective response rate of placebo in randomized controlled trials of anticancer medicines
Bibliographic record
Abstract
Background: Spontaneous regression of advanced solid tumors is infrequent but may occur. Quantifying response rates from placebo in cancer drug trials may provide important information for physicians, patients, and regulators. We aimed to provide a pooled placebo response rate from drug trials in advanced solid tumors. Methods: We pooled the overall response rate (ORR), complete response rate (CR) and partial response rates (PR) in the placebo arm of placebo-controlled randomized controlled trials (RCTs) of cancer drugs for advanced solid tumors published during 2015-2021 using random-effects model. Findings: 45 phase 3 RCTs including 5684 patients on placebo met our inclusion criteria and formed the study cohort. The pooled overall ORR, CR and PR rates in the placebo arm were 1% (95% CI, 0%-2%), 0% (95% CI, 0%-0%), and 1% (95% CI, 0%-2%) respectively. Higher placebo responses were observed in prostate cancer and sarcoma trials. Interpretation: Overall, 1% patients with advanced solid tumors can expect to achieve some response even in absence of treatment. However, complete regression without treatment is extremely rare, almost zero percent. This information will be helpful to patients in their decisions, as well as regulators in evaluating cancer drugs' efficacy based on response rates alone. Funding: None.
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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 | 0.531 | 0.649 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".