Can Oncologists Predict the Efficacy of Treatments in Randomized Trials?
Bibliographic record
Abstract
BACKGROUND: Decisions about trial funding, ethical approval, or clinical practice guideline recommendations require expert judgments about the potential efficacy of new treatments. We tested whether individual and aggregated expert opinion of oncologists could predict reliably the efficacy of cancer treatments tested in randomized controlled trials. MATERIALS AND METHODS: An international sample of 137 oncologists specializing in genitourinary, lung, and colorectal cancer provided forecasts on primary outcome attainment for five active randomized cancer trials within their subspecialty; skill was assessed using Brier scores (BS), which measure the average squared deviation between forecasts and outcomes. RESULTS: A total of 40% of trials in our sample reported positive primary outcomes. Experts generally anticipated this overall frequency (mean forecast, 34%). Individual experts on average outperformed random predictions (mean BS = 0.29 [95% confidence interval (CI), 0.28-0.33] vs. 0.33) but underperformed prediction algorithms that always guessed 50% (BS = 0.25) or that were trained on base rates (BS = 0.19). Aggregating forecasts improved accuracy (BS = 0.25; 95% CI, 0.16-0.36]). Neither individual experts nor aggregated predictions showed appreciable discrimination between positive and nonpositive trials (area under the curve of a receiver operating characteristic curve, 0.52 and 0.43, respectively). CONCLUSION: These findings are based on a limited sample of trials. However, they reinforce the importance of basing research and policy decisions on the results of randomized trials rather than expert opinion or low-level evidence. IMPLICATIONS FOR PRACTICE: Predictions of oncologists, either individually or in the aggregate, did not anticipate reliably outcomes for randomized trials in cancer. These findings suggest that pooled expert opinion about treatment efficacy is no substitute for randomized trials. They also underscore the challenges of using expert opinion to prioritize interventions for clinical trials or to make recommendations in clinical practice guidelines.
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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 | 0.368 | 0.751 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".