Modelling the Survival Outcomes of Immuno-Oncology Drugs in Economic Evaluations: A Systematic Approach to Data Analysis and Extrapolation
Bibliographic record
Abstract
BACKGROUND: New immuno-oncology (I-O) therapies that harness the immune system to fight cancer call for a re-examination of the traditional parametric techniques used to model survival from clinical trial data. More flexible approaches are needed to capture the characteristic I-O pattern of delayed treatment effects and, for a subset of patients, the plateau of long-term survival. OBJECTIVES: Using a systematic approach to data management and analysis, the study assessed the applicability of traditional and flexible approaches and, as a test case of flexible methods, investigated the suitability of restricted cubic splines (RCS) to model progression-free survival (PFS) in I-O therapy. METHODS: The goodness of fit of each survival function was tested on data from the CheckMate 067 trial of monotherapy versus combination therapy (nivolumab/ipilimumab) in metastatic melanoma using visual inspection and statistical tests. Extrapolations were validated using long-term data for ipilimumab. RESULTS: Modelled PFS estimates using traditional methods did not provide a good fit to the Kaplan-Meier (K-M) curve. RCS estimates fit the K-M curves well, particularly for the plateau phase. RCS with six knots provided the best overall fit, but RCS with one knot performed best at the plateau phase and was preferred on the grounds of parsimony. CONCLUSIONS: RCS models represent a valuable addition to the range of flexible approaches available to model survival when assessing the effectiveness and cost-effectiveness of I-O therapy. A systematic approach to data analysis is recommended to compare the suitability of different approaches for different diseases and treatment regimens.
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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 | 0.200 | 0.328 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".