Estimands for atopic dermatitis clinical trials: Expert opinion on the importance of intercurrent events
Bibliographic record
Abstract
Despite the emergence of novel targeted treatments for atopic dermatitis (AD), there is a lack of guidelines on standardizing analysis of clinical trial data. To define and estimate meaningful treatment comparisons, several factors, including intercurrent events, must be taken into account. Intercurrent events are defined as events occurring after treatment initiation that affect either the interpretation or existence of the measurements associated with clinical questions of interest. Due to the relapsing, unpredictable nature of AD, intercurrent events frequently occur in AD trials, such as use of rescue therapy for intense itch and sleep deprivation. Despite the impact of intercurrent events in AD, they are often handled in an inconsistent manner across trials, which limits results interpretation. The estimand framework is increasingly used to estimate treatment effects while accounting for intercurrent events. This review explores how guidance from the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) on the use of estimands can be applied to support AD clinical trial design and analysis. We propose that estimands are used in AD trials and defined early during trial design. The use of estimands can provide clinicians with interventional trial results that are more reflective of clinical practice, help facilitate comparisons across clinical trials, and are more informative to enable improved treatment selection for patients.
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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 | 0.130 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".