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Time-to-Event Modeling for Survival Prediction of Osimertinib as the First- and Second-Line Therapy
Sungjae Lee1,2, Heungjo Kim1,2, Hongjae Lee1,2
1Department of Pharmacy and Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon 21983, Republic of Korea.
Journal of Clinical Medicine
|June 26, 2025
Summary
This study predicts survival rates for Osimertinib in non-small cell lung cancer (NSCLC) using time-to-event models. Results show improved survival with Osimertinib, with notable ethnic differences in efficacy.
Area of Science:
- Oncology
- Pharmacology
- Biostatistics
Background:
- Osimertinib is a targeted therapy for specific non-small cell lung cancer (NSCLC) mutations.
- Predicting long-term survival outcomes is crucial for treatment planning and patient counseling.
Purpose of the Study:
- To predict overall survival (OS) and progression-free survival (PFS) for Osimertinib as both first- and second-line therapy in NSCLC.
- To analyze survival data using established time-to-event modeling techniques.
Main Methods:
- Systematic literature review of randomized clinical trials (RCTs) in PubMed and Cochrane Library up to May 2023.
- Extraction of Kaplan-Meier curves for OS and PFS from relevant RCTs.
- Application of graphic reconstructive algorithms and time-to-event models (Weibull, log-logistic) for survival prediction.
Main Results:
- Predicted median OS: 36.35 months (1st-line) and 27.46 months (2nd-line).
- Predicted median PFS: 18.11 months (1st-line) and 10.35 months (2nd-line).
- Predicted 5-year survival rates: 23% (1st-line), with higher rates observed in non-Asian patients.
Conclusions:
- Time-to-event models accurately predict Osimertinib survival outcomes in NSCLC.
- Observed ethnic variations in treatment efficacy suggest personalized treatment approaches may be beneficial.
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