Machine Learning Approaches to Personalized Therapy for Advanced Non-small Cell Lung Cancer With Real-World Data

Summary

This research will leverage machine learning (ML) and causal inference techniques applied to real-world data (RWD) to generate evidence that personalizes treatment strategies for patients with advanced non-small cell lung cancer (aNSCLC). Rather than influencing regulatory decisions or clinical guidelines, the goal of this trial is to refine treatment selection among existing therapeutic options, ensuring that care is tailored to individual patient characteristics. Additionally, by generating real-world evidence, these findings will inform the design and implementation of future clinical trials. Importantly, the methodological advancements will establish a pipeline that extends beyond aNSCLC, facilitating the identification of optimal dynamic treatment regimes (DTRs) for other complex diseases.

Eligibility

Inclusion Criteria

Subjects must meet all of the following eligibility criteria:

* Diagnosed with advanced NSCLC between January 1, 2011, and June 30, 2024.
* Follow-up available until December 31, 2024, with a minimum potential follow-up period of at least six months.

Exclusion Criteria

Subjects meeting any of the following criteria at baseline will be excluded:

* Fewer than one day of follow-up post-initiation of first-line (1L) therapy.
* Presence of a targetable mutation, including ALK, BRAF, EGFR, KRAS, or ROS1.
* PD-L1 expression \<50% at baseline (restricted to patients with PD-L1 ≥50%).
* First-line treatment limited to immunotherapy or chemoimmunotherapy (excluding other treatment regimens).
* Patients receiving second-line (2L) treatment, including those enrolled in a clinical study.

Conditions3

Locations1 site

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