EligibleTrials

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

Recruiting · NCT06934343 · Observational (researchers observe without assigning treatment) · Lead sponsor: University of Utah

View the official record on ClinicalTrials.gov →
Lung cancer

What this trial is about

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.

Who can take part

Age range
No age limits stated
Sex
All (male and female)
Healthy volunteers
No - a diagnosis or condition is required
Phase
Not specified
Study type
Observational (researchers observe without assigning treatment)

Inclusion criteria

Exclusion criteria

Where it is running

1 location listed across 1 US state.

Read the full protocol, contacts and eligibility on ClinicalTrials.gov →

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