The detailed clinical objectives approach to designing clinical trials and choosing estimands
Bibliographic record
Abstract
Objective setting is a necessary early step in the development of a clinical trial. ICH E9(R1) notes that the clinical objectives of a trial lead directly to the choice of estimands but barely discusses objectives themselves. Indeed, there is very little guidance anywhere in literature about objectives in clinical trials. This article identifies the substantial overlap between description of estimands and high quality definitions of objectives. It consequently shows that the estimand is decided by the precise choice of trial objective, and that therefore estimand decisions should be made at the objective level. The Detailed Clinical Objectives approach is proposed to support this. It emphasises clarity, specificity and a clinical focus when choosing and documenting objectives. Template text and examples are included to provide guidance on how it can be used in real trials. Finally, we describe objective-driven trial design, emphasising how strong objective setting establishes an important foundation for rigorous trial design discussions, logistical and operational decision-making during trial preparations, and clear communication of results and conclusions at the end of the trial. Highlighting the distinctions between objectives and estimands, we note how an objective-based framework can build on the ICH E9(R1) estimand framework to address many of its unanswered questions.
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 | 0.341 | 0.403 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".