Selection effects in randomized trials with count data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Selection criteria are specified in clinical trials to define the study population from which the sample will be obtained. It is common for one of these criteria to be based on historical or baseline measurements of the clinical sign or symptom that will serve as the response variable in the trial. The effect of such selection criteria has been studied extensively for normally distributed responses, but less is known about the situation in which the response is a count or a possibly recurrent event. In this paper we examine the bias and relative efficiency of some common methods of analysis for count data in the presence of selection criteria. The investigation is carried out using asymptotic theory pertaining to misspecified models and by simulation. Applications involving data from an epilepsy trial and a study of transient myocardial ischaemia illustrate the effect of ignoring the selection mechanism.
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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 | 0.042 | 0.727 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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