Using exponential decay to model survival curves in immunotherapy trials.
Bibliographic record
Abstract
e14698 Background: In oncology trials, survival outcomes, specifically overall survival (OS) and progression-free survival (PFS), are commonly assessed using the Kaplan–Meier estimator. Survival curves in immunotherapy trials for advanced solid tumors frequently display first-order kinetics, suitable for exponential decay modeling. This study utilizes such models on early trial data to predict median OS and PFS, aiming to yield earlier actionable insights. Methods: we searched PubMed to identify immunotherapy trials focusing on advanced solid tumors. Survival curves, along with actual OS and PFS data, were extracted and segmented into immunotherapy and control groups. An exponential decay model was fitted to the initial quartile of events, from which median OS and PFS were extrapolated. These forecasts were then benchmarked against documented trial results. Results: Our analysis included 347 subsets from 122 immunotherapy trials. We observed strong correlation coefficients between actual and predicted OS (0.882 for the immunotherapy group, 0.784 for the control group) and PFS (0.746 for the immunotherapy group, 0.791 for the control group). The predictive models were formulated as follows: Immunotherapy: OS = 0.867 × predicted OS (pOS) + 2.623 ⋅ Control: OS = 0.623 × pOS + 3.731; and Immunotherapy: PFS = 0.583 × predicted PFS (pPFS) + 1.827 ⋅ Control: PFS = 0.519 × pPFS + 1.901. Conclusions: The notable correlation between the actual and predicted survival outcomes supports the efficacy of exponential decay models in forecasting median OS and PFS from preliminary trial data. The consistency observed across trials underscores the model's potential as a predictive tool in clinical research. Further prospective studies are warranted to validate these predictive models and explore their implications for trial design and therapeutic decision-making in oncology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.116 | 0.386 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".