Health Studies

Hongyuan Cao

Assistant Professor, Biostatistics

(773) 834-0750

Hongyuan Cao has two major methodological research interests. The first is in statistical and computational methods for large complex data sets. The second is in longitudinal and survival data analysis. Her collaborative interests include pharmacogenomics, arsenic exposure, and cancer risk.

  • Develop powerful large-scale multiple testing methods under dependence
  • Study the effects of longitudinal biomarkers to time-to-event outcome
  • Develop robust methods adjusting for confounders and omitted variables in high dimensional setting

Lin Chen

Assistant Professor, Biostatistics

(773) 702-1626

Lin Chen's research interests focus on developing statistical methods for high-dimensional genomics data and exploring the theory behind them. Always motivated by real data problems, Dr. Chen collaborates with epidemiologists, geneticists and other scientists and wishes to contribute to the statistical methodology development that facilitates the understanding of both complex disease etiology and molecular biology. Specifically, she is interested in:

  • Set-based association testing methods with next-generation sequencing data
  • Effects of population stratification on gene-based association tests with rare variants in sequencing studies
  • Regularized methods for estimation and testing in high-dimensional data, especially when sample size is limited
  • Missing data with non-random missing patterns in proteomics data

James Dignam

Associate Professor, Biostatistics

(773) 834-3162

  • Cancer clinical trial design and conduct. Group Statistician for the Radiation Therapy Oncology Group (RTOG)
  • Influence of racial/ethnic background and lifestyle factors on cancer prognosis
  • Competing risks and multiple endpoints in survival analysis

Robert Gibbons

Professor of Biostatistics

(773) 834-8692

Robert Gibbons major research interests are in the areas of biostatistics, environmental statistics, and psychometrics.   

  • Development of linear and non-linear mixed effects regression models for analysis of continuous and discrete longitudinal data.
  • Statistical methods for analysis of environmental monitoring data.
  • Inter-laboratory calibration.
  • Statistical methods for the analysis of functional magnetic imaging data.
  • Item response theory and computerized adaptive testing.
  • Statistical methods in pharmacoepidemiology and drug safety.

Ronald Thisted

Professor, Department of Health Studies

773 834-1242

Ronald Thisted's major research interests are in the areas of biostatistics and epidemiology, statistical computation, and effectiveness of medical interventions.

  • Study regression methods for paired data with ordered categorical outcomes, problems of multiple inference in clinical trials, methods for combining information (meta-analysis) concerning diagnostic tests such as those used in nuclear medicine, and assessment of causal relationships associated with rare but catastrophic events such as sudden death in children.
  • Current work in statistical computation includes data structures for bibliographic databases, electronic publishing, computational aspects of meta-analysis, and improved design of Monte Carlo studies.
  • Works related to health outcomes include comparative assessment of prognoses that result from different treatments for prostate cancer; assessment of effectiveness for prostate-specific antigen tests for screening, diagnosis, and follow-up of prostate cancer; short- and long-term effectiveness of treatments for degenerative disease of the lumbar spine; and relative benefits of SPECT imaging relative to standard diagnostics in epilepsy and dementia.