Tianxi Cai, ScD
Professor of Biomedical Informatics, Harvard Medical School
John Rock Professor of Population and Translational Data Sciences, Harvard T.H. Chan School of Public Health
Director, Translational Data Science Center for a Learning Health System (CELEHS)
Tianxi Cai is a major player in developing analytical tools for mining EHR data and predictive modeling with biomedical data. She provides statistical leadership on several large-scale projects, including the NIH-funded Undiagnosed Diseases Network at DBMI. Cai's research lab develops novel statistical and machine learning methods for several areas including clinical trials, real world evidence, and personalized medicine using genomic and phenomic data. Cai received her ScD in Biostatistics at Harvard and was an assistant professor at the University of Washington before returning to Harvard as a faculty member in 2002.
DBMI Research Areas
Landmark Prediction of Long Term Survival Incorporating Short Term Event Time Information.
Genetic susceptibility to coronary heart disease in type 2 diabetes: 3 independent studies.
Authors: Qi L, Parast L, Cai T, Powers C, Gervino EV, Hauser TH, Hu FB, Doria A.
J Am Coll Cardiol
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J Am Coll Cardiol
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Graphical procedures for evaluating overall and subject-specific incremental values from new predictors with censored event time data.
Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies.
Authors: Lin X, Cai T, Wu MC, Zhou Q, Liu G, Christiani DC, Lin X.
Genet Epidemiol
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Genet Epidemiol
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Comparing costs associated with risk stratification rules for t-year survival.
Kernel machine approach to testing the significance of multiple genetic markers for risk prediction.
Rare-variant association testing for sequencing data with the sequence kernel association test.
Robust prediction of t-year survival with data from multiple studies.
The highest confidence density region and its usage for joint inferences about constrained parameters.
On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.
