Testing for the presence of cured patients: a simulation study
Why this work is in the frame
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Bibliographic record
Abstract
An important but difficult problem in clinical trials is to determine the presence of cured patients when long-term survivors are observed. The likelihood ratio test has been studied for this purpose in the gamma mixture model. However, its asymptotic null distribution is not readily available due to a violation of boundary conditions in the standard asymptotic theory. In this paper, a simulation study is employed to examine a proposed asymptotic null distribution of the likelihood ratio test. We find that the distribution can also be used to approximate the asymptotic null distribution of the likelihood ratio test in the Weibull and log-normal mixture models when the censoring rate is not too light. However, the simulation study also shows that null distribution of the likelihood ratio test deviates significantly from the suggested distribution under moderate sample sizes when the censoring rate is small or the hazard rate is large. Consequently caution is needed in this case to determine the presence of cured patients. Finally, the results are used to confirm the presence of cured patients in a leukaemia study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it