Number needed to treat (NNT) as a measure of drug benefit: Lenalidomide versus bortezomib for treatment of relapsed/refractory multiple myeloma (MM).
Bibliographic record
Abstract
e18562 Background: Lenalidomide(LEN) and bortezomib(BORT) are active agents in the treatment of refractory MM. The former is administered 25 mg/day orally on days 1-21 of repeated 28-day cycles. The latter as a 1.3 mg/m^2 intravenous dose on days 1, 4, 8 and 11 for eight, three week cycles. Both agents have demonstrated an improvement in disease response, progression free (PFS) and overall survival (OS) in the refractory MM setting. NNT represents the number of patients that need to be treated with a new intervention in order to have one additional patient deriving benefit, and is a powerful approach that can be used to make sense of numerical results from clinical trials. In this analysis, the NNT approach was used to compare LEN and BORT in patients with relapsed MM. Methods: The pivotal randomized trials for LEN (Weber, Dimopoulos, 2007) and BORT (Richardson, 2005) were reviewed. A key requirement for a NNT comparative analysis is that all outcomes be measured against a comparable control. The NNT was then calculated for both agents with respect to disease response and PFS at 12 months by taking the reciprocal of the absolute differences in treatment effect between the experimental therapies and the control group. Results: For a disease response, the NNT for LEN and BORT was 3 and 5 patients respectively; to have one additional patient achieving a disease response. A difference in the NNT was also noted for PFS at 12 months; only 3 patients would need to be treated with LEN for an additional patient remaining progression free compared to 9 with BORT. This is a relevant finding because it suggests a 3 fold relative increase in clinical benefit in patients treat with LEN compared to BORT. Conclusions: In situations of multiple numerical outcomes from randomized trials, NNT approach is a simple and effective method to express the findings in a clinically meaningful way. In this analysis, it appears that more patients treated with LEN are likely to respond and to achieve a 12 month PFS than comparable patients treated with BORT. Possible reasons for this effect include differences in the trial population, extent of disease, and/or true differences in efficacy.
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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 | 0.043 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".