Evaluating Treatment Effect in Multicenter Trials with Small Centers Using Survival Modeling
Bibliographic record
Abstract
Clinical trials of rare diseases commonly enlist several centers to achieve recruitment goals. The aim of this study is to examine the estimation of treatment effects for survival outcomes in multicenter clinical trials with varying numbers of centers and few patients per center for rarer disease outcomes (i.e. rare cancers). We modeled the heterogeneity between centers using Cox frailty models to account for the variability in patients and patient care between centers and examined measures of model fit via smoothed functions of a prognostic factor. Through a simulation study, we were able to examine the consequence of having only a few centers or a few patients per center on the treatment and prognostic factor effects and model performance indices. Overall, we found it is preferable to have more patients per site and more sites in a multicenter trial as expected. However, having a few patients per site is feasible if there are many sites in a trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.156 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".