An Assessment of Methods to Combine Published Survival Curves
Bibliographic record
Abstract
PURPOSE: To assess the accuracies of different techniques for combining published survival curves, for use in disease modeling applications. METHODS: Five methods were identified: 1) iterative generalized least-squares (IGLS), 2) meta-analysis of failure-time data with adjustment for covariates (MFD), 3) nonlinear regression (NLR), 4) log relative risk (LRR), and 5) weighted LRR (w-LRR). Each method was used to combine the survival curves from eight single-arm Phase II trials of chemotherapy in 918 patients with advanced non-small-cell lung cancer (NSCLC). The resulting summary curves were compared with the curve calculated from the corresponding individual patient data (IPD). RESULTS: All methods were able to produce accurate summary survival curves statistically similar to the IPD-derived curve. Maximum discrepancies ranged from 1.8% to 4.7%. MFD appeared to be the most accurate when censoring information was complete. Characteristics of the component trials that adversely affected the accuracies of the different techniques were 1) a high proportion of censored observations (MFD); 2) variability in the length of follow-up (IGLS, NLR, LRR, w-LRR); and 3) the heterogeneity of the treatment results (NLR, w-LRR). CONCLUSIONS: All methods were able to accurately reproduce summary survival curves from the published literature. The best method depends on characteristics of the data and the purpose of the analysis.
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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 | 0.341 | 0.685 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".