DELTA<sup>2</sup> guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial
Bibliographic record
Abstract
Randomised controlled trials are considered to be the best method to assess comparative clinical efficacy and effectiveness, and can be a key source of data for estimating cost effectiveness. Central to the design of a randomised controlled trial is an a priori sample size calculation, which ensures that the study has a high probability of achieving its prespecified main objective. Beyond pure statistical or scientific concerns, it is ethically imperative that an appropriate number of study participants be recruited, to avoid imposing the burdens of a clinical trial on more patients than necessary. The scientific concern is satisfied and the ethical imperative is further addressed by the specification of a target difference between treatments that is considered realistic or important by one or more key stakeholder groups. The sample size calculation ensures that the trial will have the required statistical power to identify whether a difference of a particular magnitude exists. In this article, the key messages from the DELTA2 guidance on determining the target difference and sample size calculation for a randomised controlled trial are presented. Recommendations for the subsequent reporting of the sample size calculation are also provided.
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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 | 0.218 | 0.585 |
| Meta-epidemiology (narrow) | 0.006 | 0.011 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.036 | 0.028 |
| Insufficient payload (model declined to judge) | 0.119 | 0.101 |
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".