Qixuan Chen, PhD

  • Associate Professor of Biostatistics
212-342-1245

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Overview

Qixuan Chen, PhD, is Associate Professor of Biostatistics at Columbia University Mailman School of Public Health. She obtained her PhD in Biostatistics from the University of Michigan in 2009, with training in both biostatistics and survey sampling. She is an expert in the development and application of statistical methods for analyzing complex surveys and handling data with missing values or measurement errors. She has worked extensively with scientific colleagues on the design and implementation of longitudinal and cross-sectional surveys at local, national, and international levels.

Dr. Chen’s research on survey sampling focuses on leveraging auxiliary information about study populations to improve survey design and analysis. This includes integrating administrative records with survey samples, combining probability and nonprobability surveys, and using aggregated population data to strengthen survey inference. In this work, she has pioneered a Bayesian predictive inference approach, utilizing regularized regression and machine learning techniques. In addition, Dr. Chen addresses issues related to missing data and measurement error, developing variable selection methods for multiply imputed data, multiple imputation techniques for measurement error in exposure mixtures, and contamination models to correct for measurement error in serial dilution assays. To promote collaboration and transparency, Dr. Chen provides publicly accessible dashboards, software, and platforms. Together with her students, she has developed the New York City Neighborhood COVID-19 dashboard and a radiology diagnostics dashboard you can view bloew:

Academic Appointments

  • Associate Professor of Biostatistics

Credentials & Experience

Education & Training

  • BA, 2002 Economics (International Trade), Nankai University
  • MS, 2004 Applied Statistics, Bowling Green State University
  • PhD, 2009 Biostatistics, University of Michigan

Committees, Societies, Councils

  • Member of American Statistical Association (ASA)
  • Member of International Biometric Society Eastern North American Region (ENAR)
  • Member of International Chinese Statistical Association (ICSA)

Editorial Boards

Associate Editor, Journal of the Royal Statistical Society: Series C

Honors & Awards

  • 2016: Career Development Award, The NIEHS Center for Environmental Health in Northern Manhattan, Mailman School of Public Health
  • 2010: Department of Biostatistics Teaching Award, Mailman School of Public Health
  • 2010: Calderone Research Prize for Junior Faculty, Mailman School of Public Health
  • 2009: Edward C. Bryant Scholarship, American Statistical Association
  • 2007: Otto Hutzinger Award, 27th International Symposium on Halogenated Persistent Organic Pollutants, Tokyo, Japan

Research

Research Interests

  • Biostatistical Methods
  • Community Health
  • Data integration
  • Environmental Health
  • Measurement Error
  • Mental Health
  • Missing Data
  • Substance Use

Grants

Present Grants

Improving the analysis and use of contaminated immunoassays: from methods development to implementation (R01ES035784)

Past Grants

Bayesian exposure-response analysis for immunoassays data with measurement errors (R21ES029668)

Selected Publications

Yu, Y., Little, R.J.A., Perzanowski, M., Chen, Q. (2024). Multiple imputation of more than one environmental exposure with non-differential measurement error, Biostatistics, 25(2):306-322.

Liu, Y., Gelman, A., Chen, Q. (2023). Inference from non-random samples using Bayesian machine learning, Journal of Survey Statistics and Methodology, 11(2):433–455.

Anthopolos, R., Chen, Q., Sedransk, J., Thompson, M., Meng, G., Galea, S. (2023). A Bayesian growth mixture model for complex survey data: clustering post-disaster PTSD trajectories, Annals of Applied Statistics, 17(3): 2494-2514

Yao, Y., Ogden, T., Zeng, C., Chen, Q. (2023). Bivariate hierarchical Bayesian model for combining summary measures from multiple sources, Annals of Applied Statistics, 17(2): 1782-1800.

Liu, Y. and Chen, Q. (2020). Bayesian inference of finite population quantiles for skewed survey data using skew-normal penalized spline regression, Journal of Survey Statistics and Methodology, 8(4), 792-816.

Chen, Q., Elliott, M., Haziza, D., Yang, Y., Ghosh, M., Little, R. J. A., Sedransk, J., and Thompson, M. (2017). Approaches to improving survey-weighted estimates, Statistical Science, 32(2), 227-248.

Chen, Q., Gelman, A., Tracy, M., Norris, F., and Galea, S. (2015). Incorporating the sampling design in weighting adjustments for panel attrition, Statistics in Medicine, 34, 3637-3647.

Chen, Q. and Wang, S. (2013). Variable selection for multiply-imputed data with application to dioxin exposure study, Statistics in Medicine, 32, 3646-59.

Chen, Q., Elliott, M. R., and Little, R. J. A. (2012). Bayesian inference of finite population quantiles from unequal probability samples, Survey Methodology, 38, 203-214.

Chen, Q., Elliott, M. R., and Little, R. J. A. (2010). Bayesian penalized spline model-based inference for finite population proportion in unequal probability sampling, Survey Methodology, 36, 23-34.