Potential for extreme bias due to outcome misclassification in relative measures of effect for rare time-to-event outcomes
Bibliographic record
Abstract
Time-to-event outcomes are widely used in clinical and epidemiological research. For instance, studies of medical product safety often involve comparative analyses of rare time-to-event outcomes. The effects of misclassified outcomes and error in survival times for time-to-event data have not been widely investigated. In this Monte Carlo simulation study, we compared the relative bias of absolute and relative measures of effect under varying degrees of outcome misclassification, outcome incidences, direction of error in survival times, and the time point of inference. Relative measures of effect were susceptible to considerable downward bias, which was larger when the outcome incidence and specificity were lower, error in survival times led to earlier times, time point of inference was earlier, and the estimation excluded samples for which an estimate could not be obtained. For absolute measures of effect, the pattern of bias was much simpler, greater downward bias was primarily a function of the degree of sensitivity. The results suggest when the outcome incidence is rare, specificity and sensitivity are high, absolute measures of effect may be preferable to relative measures of effect.
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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 | 0.415 | 0.692 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".