Reflections on using non-inferiority randomised placebo controlled trials in assessing cardiovascular safety of new agents for treatment of type 2 diabetes
Bibliographic record
Abstract
The 2008 Food and Drug Administration (FDA) guidance to industry requires experimental evidence that new agents to treat type 2 diabetes do not have an unacceptable increase in cardiovascular risk. They specify this unacceptable increase to be a risk ratio of 1.3 in non-inferiority trials which may use placebo control. Clinically, this means that if a new agent achieves this threshold of not being 30% worse than placebo it is declared 'non-inferior'. This guidance was in response to safety concerns raised about medications approved on their basis of reducing glycated haemoglobin alone. There was concern that this FDA guidance would stifle new drugs coming to market. On the contrary, there have been a number of exciting new classes of agents approved with improved confidence that they reduce glycated haemoglobin, and that they also do not excessively increase cardiovascular risk. Cardiovascular safety trials have been conducted for a number of novel medications using a non-inferiority approach. However, clinicians need to recognise that the results of non-inferiority trials are not as credible as superiority trials. It is important to closely review the trials before accepting claims of 'non-inferiority' or 'cardiac neutrality' especially when these studies are often compared with placebo, and may be accepting estimates of effect which span potentially clinically meaningful harm. There are compelling reasons to further investigate agents showing promise in non-inferiority trials with superiority trials, which include prespecified subgroups, and with sufficient power and duration to provide robust estimates of harms and benefits to inform clinical decision-making.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.668 | 0.793 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.024 | 0.051 |
| Open science | 0.025 | 0.010 |
| Research integrity | 0.086 | 0.155 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".