Hypothesis Testing in Superiority, Noninferiority, and Equivalence Clinical Trials
Bibliographic record
Abstract
In medical research, it is important to be able to examine whether there is a significant difference between two samples. With this, establishing an appropriate hypothesis is a critical, basic step for correct interpretation of results in inferential statistical data analysis. It is important to note that the aim of hypothesis testing is not to "accept" or "reject" the null hypothesis but to gauge the likelihood that the observed difference is genuine if the null hypothesis is true.Traditionally, the null hypothesis assumes that there is no statistically significant difference between the two groups. It has become more difficult to develop new treatments that are better than the standard of care. This review article summarizes and explains the methodology of the different types of clinical trials regarding the relevant basic statistical concepts and hypothesis testing.
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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 | 0.104 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| 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".