On Comparing Survival Curves with Right-Censored Data According to the Events Occur at the Beginning, in the Middle and at the End of Study Period
Bibliographic record
Abstract
In clinical practice the event of interest does not always occur equally across the study time period. Depending on the disease being investigated, the event that is of interest can occur intensively in different periods of the follow-up time. In such cases, choosing the correct survival comparison test has importance. This study aims to examine and discuss the results of survival comparison tests under some certain circumstances. A simulation study was conducted. We discussed the result of different tests such as Logrank, Gehan-Wilcoxon, Tarone-Ware, Peto-Peto, Modified Peto-Peto tests and tests belonging to Fleming-Harrington test family with (p, q) values; (1, 0), (0.5, 0.5), (1, 1), (0, 1) ve (0.5, 2) by means of Type I error rate that obtained from simulation study, when the event of interest occurred intensively at the beginning of the study, in the middle of the study and at the end of the study time period. As a result of simulation study, Type I error rate of tests is generally lower or higher than the nominal value. In the light of the results, it is proposed to re-examine the tests for cases where events are observed intensively at the beginning, middle and late periods, to carry out new simulation studies and to develop new tests if necessary.
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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 | 0.066 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".