Yuan Chen, PhD

Assistant Attending Biostatistician
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Yuan Chen

Education

Columbia University

Current Research Interest

Dr. Chen’s main research interest lies in developing statistical and machine learning methods to facilitate precision medicine. She is interested in integrating evidence from multiple data domains and data resources to address patient heterogeneity and study individualized treatment strategies to improve treatment response. She is also interested in dynamic treatment regimes to promote recurrent disease control and management. Adaptive personalized intervention strategies can be tailored utilizing patients’ longitudinal or time series data, e.g., data from sequential multiple assignment randomized trials, electronic health record data, and mobile health data, with careful causal justifications. Recently, she also worked on spatial-temporal models to understand COVID-19 transmission and risk factors. Dr. Chen collaborates with investigators in the Breast Medicine Service for design and analysis of retrospective and prospective studies.

Publications

Selected peer-reviewed publications:

  1. Chen, Y., Fei, W., Qinxia, W., Zeng, D., & Wang, Y. (2021). Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model. Advances in Neural Information Processing Systems (NeurIPS), 34.
  2. Chen, Y., Zeng, D., & Wang, Y. (2021). Learning individualized treatment rules for multiple-domain latent outcomes. Journal of the American Statistical Association, 116(533), 269–282.
  3. Chen, Y., Wang, Y., & Zeng, D. (2020). Synthesizing independent stagewise trials for optimal dynamic treatment regimes. Statistics in Medicine, 39(28), 4107–4119.  
  4. Chen, Y., Zeng, D., Xu, T., & Wang, Y. (2020). Representation learning for integrating multi-domain outcomes to optimize individualized treatment. Advances in Neural Information Processing Systems (NeurIPS), 33.
  5. Chen, Y., Liu, Y., Zeng, D., & Wang, Y. (2020). Statistical Learning Methods for Optimizing Dynamic Treatment Regimes in Subgroup Identification. In Design and Analysis of Subgroups with Biopharmaceutical Applications, pp. 271-297. Springer, Cham.

Disclosures

Doctors and faculty members often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community.

MSK requires doctors and faculty members to report (“disclose”) the relationships and financial interests they have with external entities. As a commitment to transparency with our community, we make that information available to the public.

Yuan Chen discloses the following relationships and financial interests:

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