Machine Learning Approaches to Personalized Therapy for Advanced Non-small Cell Lung Cancer With Real-World Data
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 144,400
- 试验地点
- 1
- 主要终点
- Overall Survival (OS)
研究概览
简要总结
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.
详细描述
The proposed research will enhance patient-centered outcomes research (PCOR) and comparative effectiveness research (CER) methodologies by addressing two key challenges: (1) appropriate handling of missing EHR data and (2) rigorous causal inference techniques for sequential treatment strategies. By focusing on treatment strategies tailored to individual patients and incorporating patient-reported outcomes (PROs), this study is fundamentally patient-centered. Furthermore, the research is guided by practicing physicians, a patient advocate, and a former patient caregiver, ensuring that it remains aligned with the needs and priorities of those directly affected by aNSCLC.
This study will develop novel reinforcement learning algorithms by integrating multiply robust matching-based approaches. This study will tailor each component of DTR to optimize treatment sequences for aNSCLC patients, leveraging two large-scale, high-quality nationwide real-world electronic health record (EHR) databases: the Flatiron aNSCLC database and the CancerLinQ lung cancer database. These databases provide comprehensive clinicodemographic and longitudinal patient data.
Additionally, incorporating PRO data from two National Cancer Institute (NCI)-designated Comprehensive Cancer Centers -Huntsman Cancer Institute (HCI) and Moffitt Cancer Center (MCC) - will enable this trial to capture the patient perspective when personalizing aNSCLC care recommendations. Key outcomes will include overall survival, quality-adjusted life years (QALYs), time to second progression or death (PFS2), and time to worsening of selected PROs, all framed as time-to-event outcomes.
These methodological innovations will establish a reproducible pipeline for translating real-world evidence from large-scale EHR data into personalized DTR recommendations for aNSCLC patients and other complex disease populations.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Subjects must meet all of the following eligibility criteria:
- •Diagnosed with advanced NSCLC between January 1, 2011, and June 30,
- •Follow-up available until December 31, 2024, with a minimum potential follow-up period of at least six months.
排除标准
- •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 ROS
- •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.
研究组 & 干预措施
Flatiron database
The current study will utilize data from national EHR databases (Flatiron and CancerLinQ) and existing cohort data (HCI and MCC). Only de-identified data will be used, and no patients will be contacted during the study.
CancerLinQ database
The current study will utilize data from national EHR databases (Flatiron and CancerLinQ) and existing cohort data (HCI and MCC). Only de-identified data will be used, and no patients will be contacted during the study.
Huntsman Cancer Institute (HCI) Cohort
The current study will utilize data from national EHR databases (Flatiron and CancerLinQ) and existing cohort data (HCI and MCC). Only de-identified data will be used, and no patients will be contacted during the study.
Moffitt Cancer Center (MCC) Cohort
The current study will utilize data from national EHR databases (Flatiron and CancerLinQ) and existing cohort data (HCI and MCC). Only de-identified data will be used, and no patients will be contacted during the study.
结局指标
主要结局
Overall Survival (OS)
时间窗: From the initiation of first-line therapy to death or the last follow-up, whichever occurs first, up to 10 years.
Mortality is the primary concern in aNSCLC care. This study will track survival from the initiation of first-line therapy to death or the last follow-up, whichever occurs first. OS will be censored at the last recorded date in the electronic health records.
次要结局
- Quality-Adjusted Life Years (QALYs)(From the initiation of first-line therapy to death or the last follow-up, whichever occurs first, up to 2 years.)
- Time to second progression or death (PFS2)(From the initiation of first-line therapy to second disease progression, death, or the last follow-up, whichever occurs first, up to 10 years.)
- National Cancer Institute (NCI) Patient-Reported Outcomes Measurement Information System-Cancer (PROMIS-Ca)(From the initiation of first-line therapy to worsening or the last follow-up, whichever occurs first, up to 10 years.)
- Edmonton Symptom Assessment System (ESAS) Outcomes(From the initiation of first-line therapy to worsening or the last follow-up, whichever occurs first, up to 10 years.)