Statistical Analyses of Mutually Exclusive Competing Risks in Neonatal Studies
Bibliographic record
Abstract
Following the well-established approach on how to deal with competing risks in the situation of time-to-event endpoints, cumulative incidences have to be used to analyse each single category of an outcome in the situation of competing risks without a time-to-event structure as well. This can be easily done by applying a simple chi-square test. Nevertheless, these categorial outcomes are usually combined to get a composed dichotomous outcome to face the problem on how to deal with a significant chi-square omnibus test in the situation of more than 1 df, i.e. > 2x2 tables. The aim of this report is to question the practice of combined, i.e. composed dichotomized, endpoints because important information is lost and the real effect of interest in confirmatory phase III studies may only become apparent in explorative secondary analyses. It is shown – by using hypothetical data and by recalculation of published phase III studies’ results – how the use of a chi-square omnibus test and the scarcely known post-hoc testing answers the real question of interest within one primary confirmatory analysis. This method reveals insight into the actual effect of a new treatment or therapy on the event of interest in the presence of a mutually exclusive competing risk
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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 | 0.245 | 0.486 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".