The analysis of treatment effects for recurring episodic conditions
Bibliographic record
Abstract
Many chronic disease processes feature acute episodic conditions which warrant therapeutic intervention to alleviate symptoms or reduce the risk of further complications. Examples of such disease processes arise in fields such as neurology, where migraineurs experience recurrent attacks of migraine, and respirology, where patients suffering from asthma, cystic fibrosis, or chronic obstructive pulmonary disease may experience recurrent exacerbations. In randomized clinical trials, patients suffering from diseases of this sort are often randomized to one of several treatments and followed over a fixed period of time, during which any episodes are treated with the assigned treatment. When the outcome of interest is a response to treatment at each episode, the data have a similar structure to longitudinal data from studies with prescheduled follow-up assessments, and it is commonplace for analyses to be based on the corresponding methodology. However, this approach ignores the fact that the timing of episodes, and hence the number observed in any given period, is stochastic. In this tutorial we demonstrate the biases that result from naive analyses, discuss analyses that account for the complete stochastic nature, and use a recent migraine trial for illustration. We conclude with some considerations for the design of randomized trials where the unit of analysis is the episode rather than the patient.
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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 | 0.113 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| 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; 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".