Test of treatment effect in pre‐drug and post‐drug count data with zero‐inflation
Bibliographic record
Abstract
A procedure for testing for treatment effect in data similar to the data on premature ventricular contractions (PVC) is presented. We consider a zero-inflated beta-binomial model. Based on this model, we develop score tests to test for treatment effect in the data in which observations in the form of counts are recorded before and after applying a therapy. Results of a small simulation experiment, to study small sample behaviour of a score test and a likelihood ratio test, are reported and the PVC data are analysed. Both the score and the likelihood ratio tests show good level properties. Either the score tests or the likelihood ratio tests can be used for testing the presence of treatment effect. The score tests, however, may be preferable because they use estimates of the parameters only under the null hypothesis and in the important range pi<0.5 power of the score test statistic S1 is slightly better than the likelihood ratio statistic LR1.
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How this classification was reachedexpand
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 | 0.004 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".