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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.475 | 0.757 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".