Estimation of expectedness: Predictive accuracy of standard therapy outcomes in randomized phase 3 studies in epithelial ovarian cancer
Bibliographic record
Abstract
BACKGROUND: The anticipated clinical outcome of the standard/control arm is an important parameter in the design of randomized phase 3 (RP3) trials to properly calculate sample size, power, and study duration. Changing patterns of care or variation in the study population enrolled may lead to a deviation from the initially anticipated outcome. The authors hypothesized that recent changes in patterns of care in epithelial ovarian cancer (EOC) have led to challenges in correctly estimating the outcome of control groups. METHODS: A systematic review of the literature was conducted for RP3 trials of EOC published between January 2000 and December 2010. The expected outcome of the control arm as well as the actual outcome achieved by this cohort was collected and a ratio (actual-over-expected ratio) was calculated. The estimation of outcome was deemed accurate if the outcome of the control arm was between 0.75 to 1.25 times the anticipated outcome. RESULTS: A total of 35 trials were eligible for analysis. Fifteen trials had survival as the primary endpoint whereas 20 had a progression-based primary endpoint. In total, 12 of 15 trials with a survival-based endpoint significantly underestimated the outcome of the control arm, whereas only 4 of 20 trials with a progression-based endpoint did. Studies with a survival endpoint underestimated outcome more frequently than those with a progression endpoint (P<.001). CONCLUSIONS: Survival of the control arm has frequently been underestimated in recent EOC RP3 trials. This underestimation means that the initial statistical assumptions of these trials may have been inaccurate. Underestimating the outcome of the control arm may result in trials being underpowered to demonstrate the absolute benefit they were designed to show.
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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 | 0.589 | 0.824 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".