ArticleCPT: pharmacometrics & systems pharmacology2025
Development of a Joint Tumor Size-Overall Survival Modeling and Simulation Framework Supporting Oncology Development Decision-Making.
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bridging tumor growth dynamics and survival in preclinical oncology drug development - a Tumor Growth Inhibition and Time-to-Event (TGI-TTE) modeling framework.Cancer chemotherapy and pharmacology · 2026Article
- Bridging Tumor Growth Dynamics and Survival in Preclinical Oncology Drug Development - A Translational Tumor Growth Inhibition and Time to Event (TGI-TTE) Modeling Framework.Research square · 2026Article
- Longitudinal Tumor Size and Survival Modeling for Exposure-Response Analysis of Drugs with Frequent Dose Reductions: Dose Justification of Abemaciclib in Patients with Metastatic Breast Cancer.Clinical pharmacology and therapeutics · 2026Article
- Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non-Small Cell Lung Cancer.JAMA network open · 2026Article
- Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor size-overall survival (TS-OS) models can support decision-making in oncology drug development by predicting long-term OS based on TS data from early data cuts and baseline patient factors. The current work describes the development of a TS-OS framework capable of predicting OS across a variety of treatment modalities and mechanisms of action in patients with non-small cell lung cancer from seven clinical studies. The presented framework jointly models TS with a bi-exponential Stein model and OS with an accelerated failure time log-normal survival model. In the corresponding link function between TS and OS, the most significant predictor of OS was the tumor growth rate (k
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