Quantitative Evaluation of the Impact of Relaxing Eligibility Criteria for Lung Cancer Based on Real-world Data

Lung Cancer Clinical Trial

Official title:

Quantitative Evaluation of the Impact of Relaxing Eligibility Criteria on the Risk-benefit Profile of Drugs for Lung Cancer Based on Real-world Data

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06314542
• Other study ID #: REC-001
• Status: Recruiting
• Start date: January 1, 2013
• Est. completion date: June 30, 2026

Study information

• Verified date: March 2024
• Source: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
• Contact: Ning Li, doctor
• Phone: 01087788713
• Email: ncctrials[@]cicams.ac.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Eligibility criteria for cancer drug trials are generally too stringent, leading to key issues such as low enrolment rates and lack of population diversity. In order to evaluate the REC of NSCLC drug trials, this study will use deep learning methods to construct a structured real-world database of NSCLC across dimensions, and quantitatively assess the independent contribution of changes in each eligibility criterion to patient numbers, clinical efficacy and safety.

Description:

Restrictive eligibility criteria in cancer drug trials result in low enrollment rates and limited population diversity. Relaxed eligibility criteria (REC) based on solid evidence is becoming necessary for stakeholders worldwide. However, the absence of high-quality, favorable evidence remains a major challenge. This study presents a protocol to quantitatively evaluate the impact of relaxing eligibility criteria in common non-small cell lung cancer (NSCLC) protocols in China, on the risk-benefit profile. This involves a detailed explanation of the rationale, framework, and design of REC.

To evaluate our REC in NSCLC drug trials, we will first construct a structured, cross-dimensional real-world NSCLC database using deep learning methods. We will then establish randomized virtual cohorts and perform benefit-risk assessment using Monte Carlo simulation and propensity matching. Shapley value will be utilized to quantitatively measure the effect of the change of each eligibility criterion on patient volume, clinical efficacy and safety.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 50000
• Est. completion date: June 30, 2026
• Est. primary completion date: December 31, 2025
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

Patients in the database were considered to be part of the real-world cohort if they were (1) diagnosed with NSCLC according to the tenth revision of the international classification of diseases (ICD-10) code; (2) diagnosed with stage IIIB, IIIC, IV NSCLC between 1 January 2013 and 31 December, 2022; (3) had at least two documented clinical visits on or after 1 January 2013.

Exclusion Criteria:

(1)NSCLC in stage I-IIIa

Related Conditions & MeSH terms

• Lung Cancer
• Lung Neoplasms

Intervention

Other:
• relaxing eligibility criteria
Quantitative evaluation of the impact of relaxing eligibility criteria on the risk-benefit profile of drugs for lung cancer based on real-world data

Locations

Country	Name	City	State
China	Cancer Hospital, Chinese Academy of Medical Sciences	Beijing

Sponsors (2)

Lead Sponsor	Collaborator
Cancer Institute and Hospital, Chinese Academy of Medical Sciences	AstraZeneca

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Scale of eligibility criteria	Lung cancer clinical trial protocol eligibility criteria	2024.12.31