Treatment crossovers in time-to-event non-inferiority randomised trials of radiotherapy in patients with breast cancer
Bibliographic record
Abstract
BACKGROUND: In non-inferiority trials of radiotherapy in patients with early stage breast cancer, it is inevitable that some patients will cross over from the experimental arm to the standard arm prior to initiation of any treatment due to complexities in treatment planning or subject preference. Although the intention-to-treat (ITT) analysis is the preferred approach for superiority trials, its role in non-inferiority trials is still under debate. This has led to the use of alternative approaches such as the per-protocol (PP) analysis or the as-treated (AT) analysis, despite the inherent biases of such approaches. METHODS: Using simulations, we investigate the effect of 2%, 5% and 10% random and non-random crossovers prior to radiotherapy initiation on the ITT, PP, AT and the combination of ITT and PP analyses with respect to type I error in trials with time-to-event outcomes. We also evaluate bias and SE of the estimates from the ITT, PP and AT approaches. RESULTS: The AT approach had the best performance in terms of type I error, but was anticonservative as non-random crossover increased. The ITT and PP approaches were anticonservative under all percentages of random and non-random crossover. Similarly, lowest bias was seen with the AT approach; however, bias increased as the percentage of non-random crossover increased. The ITT and PP had poor performance in terms of bias as crossovers increased. CONCLUSIONS: If minimal crossovers were to occur, we have shown that the AT approach has the lowest type I error rates and smallest opportunity for bias. Results of trials with a high number of crossovers should be interpreted with caution, especially when crossover is non-random. Attempts to prevent crossovers should be maximised.
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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 | 0.291 | 0.420 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".