The Impact of the Underlying Risk in Control Group and Effect Measures in Non-Inferiority Trials With Time-to-Event Data: A Simulation Study
Bibliographic record
Abstract
BACKGROUND: We designed a simulation study to assess how the conclusions of a non-inferiority trial (NIT) will change if the observed risk is different from the expected risk. METHODS: We simulated Weibull distribution time-to-event data with a true hazard ratio (HR) being equal or close to 1. The empirical margins and sample size of a hypothetical trial were chosen based on a systematic review. Setting the significance level at 5% for the two-sided confidence interval (CI), we examined the statistical power (i.e., the probabilities of the upper limit of the 95% CI falling within the margin) of using two measures at various underlying risk in the control group. RESULTS: Using the empirical margins, HRs of 1.2, 1.35 or 1.5, the statistical power is lower than 0.22 when the underlying risk in the control group is less than 10%, but the power increases along with the higher underlying risk. The predicted upper limit of the 95% CI of the difference in two Kaplan-Meier estimators (DTKME) is low when risk is low (< 20%) or high (> 80%), but reaches the highest value when risk is around 50%. When the underlying risk in the control group is lower than 10%, measures of DTKME resulted in much higher power than HR. CONCLUSIONS: When HR is the effect measure, the probability of concluding non-inferiority will increase as the underlying risk in the control group increases. When DTKME is the effect measure, the probability of concluding non-inferiority will decrease as the underlying risk in the control increases. In this case, the probability of concluding non-inferiority is at a minimum when the control risk reaches about 50%. When the risk in the control arm is less than 10%, the conclusion of an NIT is sensitive to the choice of effect measure.
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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 | 0.398 | 0.840 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".