Multivariate network meta‐analysis of survival function parameters
Bibliographic record
Abstract
BACKGROUND: Network meta-analysis (NMA) of survival data with a multidimensional treatment effect has been introduced as an alternative to NMA based on the proportional hazards assumption. However, these flexible models have some limitations, such as the use of an approximate likelihood based on discrete hazards, rather than a likelihood for individual event times. The aim of this article is to overcome the limitations and present an alternative implementation of these flexible NMA models for time-to-event outcomes with a two-step approach. METHODS: First, for each arm of every randomised controlled trial (RCT) connected in the network of evidence, reconstructed patient data are fit to alternative survival distributions, including the exponential, Weibull, Gompertz, log-normal, and log-logistic. Next, for each distribution, its scale and shape parameters are included in a multivariate NMA to obtain time-varying estimates of relative treatment effects between competing interventions. RESULTS: An illustrative analysis is presented for a network of RCTs evaluating multiple interventions for advanced melanoma regarding overall survival. Alternative survival distributions were compared based on model fit criteria. Based on the log-logistic distribution, the difference in shape and scale parameters for each treatment versus dacarbazine (DTIC) was identified and the corresponding log hazard and survival curves were presented. CONCLUSIONS: The presented two-step NMA approach provides an evidence synthesis framework for time-to-event outcomes grounded in standard practice of parametric survival analysis. The method allows for a more transparent and efficient model selection process.
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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 | 0.061 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".