Video: Evaluation of real-world response rate in clinical trial-aligned cohorts of patients with lung, colorectal, and breast cancer using machine learning

Written by The Evidence Base
Artificial intelligence and machine learning Health data Interviews ISPOR Medical devices, pharmaceuticals and vaccines Oncology and hematology Peek behind the poster Real-world evidence

Laura Dormer, Editor of The Evidence Base, speaks with Qianyi Zhang, Senior Research Scientist, Flatiron Health about the poster, ‘Evaluation of real-world response rate in clinical trial-aligned cohorts of patients with lung, colorectal, and breast cancer using machine learning’, presented at ISPOR 2024 (May 6–8, 2024; Atlanta, GA, USA).

You can view the entire video or view a specific question by entering ‘Fullscreen with Transcript’ or navigate via the ‘Visual Table of Contents’.

Questions:

  • 0:00: Introduction
  • 1:30: What were the main objectives of your study?
  • 3:51: Could you describe the data sources used in the study and how they were selected?
  • 4:37: How did you ensure that the generated real-world cohorts were aligned with the control arms of the selected trials?
  • 5:24: How was machine learning used to evaluate real-world response rates?
  • 6:01: What were the key findings of your study?
  • 8:40: What future research will be needed to take this work forward?
  • 10:06: Do you have other prior publications in the same field?
  • 11:43: Why do you think this type of research is important?

 

Further reading:
  • Ma X, Bellomo L, Magee K et al. Characterization of a Real-World Response Variable and Comparison with RECIST-Based Response Rates from Clinical Trials in Advanced NSCLC. Adv. Ther. 38, 1843–1859 (2021). https://doi.org/10.1007/s12325-021-01659-0
  • Lu Y, Langerman SS, McCain E et al. Response- and Progression-Based End Points in Trial and Observational Cohorts of Patients With NSCLC. JAMA Netw. Open. 2024;7(5):e249286. https://doi.org/10.1001/jamanetworkopen.2024.9286

 

 

Qianyi Zhang 
Senior Research Scientist, Flatiron Health

Qianyi Zhang is a Senior Research Scientist at Flatiron Health, where she has been an integral part of the team for over five years. She holds a Master’s degree in Biostatistics from Columbia University’s Mailman School of Public Health.

With nearly eight years of experience as a statistician, Qianyi specializes in study design, retrospective statistical analysis, and the application of machine learning to clinical data. At Flatiron Health, she leverages the company’s extensive, longitudinal EHR data to design and implement statistical analyses. She establishes frameworks to validate the performance of real-world endpoints and inform their development, facilitating various fit-for-use real-world evidence research in oncology. Qianyi collaborates closely with Flatiron research partners and supports life science companies to generate rigorous, high-quality real-world evidence across a variety of use cases, including internal decision-making, drug development, clinical care, market access, and commercial applications.

Before joining Flatiron Health, Qianyi gained significant experience in clinical data research, applying innovative statistical methods and machine learning models to large observational clinical cohorts. Her work in this area has been recognized and presented at numerous clinical conferences, highlighting her contributions to advancing healthcare analytics.

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Disclosures:

The opinions expressed in this feature are those of the interviewee/author and do not necessarily reflect the views of The Evidence Base® or Becaris Publishing Ltd. 

Sponsorship for this interview was provided by Flatiron Health.