Incorporating the possibility of cure into network meta-analyses: A case study from resected Stage III/IV melanoma
Bibliographic record
Abstract
In many areas of oncology, cancer drugs are now associated with long-term survivorship and mixture cure models (MCM) are increasingly being used for survival analysis. The objective of this article was to propose a methodology for conducting network meta-analysis (NMA) of MCM. This method was illustrated through a case study evaluating recurrence-free survival (RFS) with adjuvant therapy for stage III/IV resected melanoma. For the case study, the MCM NMA was conducted by: (1) fitting MCMs to each trial included within the network of evidence; and (2) incorporating the parameters of the MCMs into a multivariate NMA. Outputs included relative effect estimates for the MCM NMA as well as absolute estimates of survival (RFS), modeled within the Bayesian multivariate NMA, by incorporating absolute baseline effects of the reference treatment. The case study was intended for illustrative purposes of the MCM NMA methodology and is not meant for clinical interpretation. The case study demonstrated the feasibility of conducting an MCM NMA and highlighted key issues and considerations when conducting such analyses, including plausibility of cure, maturity of data, process for model selection, and the presentation and interpretation of results. MCM NMA provides a method of comparative survival that acknowledges the benefit newer treatments may confer on a subset of patients, resulting in long-term survival and reflection of this survival in extrapolation. In the future, this method may provide an additional metric to compare treatments that is of value to patients.
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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 | 0.062 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".