Incomplete Data in Randomized Dermatology Trials: Consequences and Statistical Methodology
Bibliographic record
Abstract
Randomized clinical trials can provide the highest level of evidence regarding the effectiveness of therapeutic interventions. When individuals in trials do not complete the planned treatment period it is often not possible to observe the desired outcomes, which results in incomplete data. Here we review various mechanisms which can lead to incomplete data, discuss the impact of these mechanisms, and present strategies for dealing with incomplete data. We discuss these issues in the context of clinical trials in dermatology and provide practical recommendations for planning and drawing conclusions from studies which could involve incomplete data.
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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 | 0.681 | 0.854 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.011 | 0.017 |
| 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".