Minimum important difference is minimally important in sample size calculations
Bibliographic record
Abstract
Performing a sample size calculation for a randomized controlled trial requires specifying an assumed benefit (that is, the mean improvement in outcomes due to the intervention) and a target power. There is a widespread belief that judgments about the minimum important difference should be used when setting the assumed benefit and thus the sample size. This belief is misguided - when the purpose of the trial is to test the null hypothesis of no treatment benefit, the only role that the minimum important difference should be given is in determining whether the sample size should be zero, that is, whether the trial should be conducted at all.The true power of the trial depends on the true benefit, so the calculated sample size will result in a true power close to the target power used in the calculation only if the assumed benefit is close to the true benefit. Hence, the assumed benefit should be set to a value that is considered a realistic estimate of the true benefit. If a trial designed using a realistic value for the assumed benefit is unlikely to demonstrate that a meaningful benefit exists, the trial should not be conducted. Any attempt to reconcile discrepancies between the realistic estimate of benefit and the minimum important difference when setting the assumed benefit merely conflates a valid sample size calculation with one based on faulty inputs and leads to a true power that fails to match the target power.When calculating sample size, trial designers should focus efforts on determining reasonable estimates of the true benefit, not on what magnitude of benefit is judged important.
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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 | 0.112 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.026 | 0.054 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".