Number needed to treat in indirect treatment comparison
Bibliographic record
Abstract
AIM: For dichotomous outcomes, odds ratio (OR) is one of the usual summary measures of indirect treatment comparison. A corresponding number needed to treat (NNT) estimate may facilitate understanding of the treatment effect. METHODS: We show how to estimate NNT based on OR results of a matching adjusted indirect comparison. We also have derived the explicit formula of its 95% CIs by applying the delta method, and as an alternative, a simulation-based method. RESULTS: The method was applied in a case study example in radioiodine-refractory differentiated thyroid cancer (RR-DTC) patients, comparing lenvatinib to sorafenib. For every two RR-DTC patients treated with lenvatinib instead of sorafenib, one fewer would have progressed and for every eight RR-DTC patients treated with lenvatinib instead of sorafenib, one fewer would have died. CONCLUSION: Using NNT to summarize the results of a matching adjusted indirect comparison can help the clinicians to better understand the results in addition to OR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".