Predictive Analytics

• Efficient Identification of Predictive Markers

• Risk Prediction under Efficient Two-phase Study Designs

• Machine Learning Approaches to Risk Prediction

• Quantifying Prediction Performance

Research Team

Cai, Tianxi

Neykov, Matey

Parast, Layla

Tian, Lu

Uno, Hajime

Wei, Lee Jen

Selected Publications

• M. Goodman, Lori B Chibnik, and T. Cai, 2019 “Variance Components Genetic Association Test for Zero-inflated Count Outcomes” Genetic Epidemiology, doi: 10.1002/gepi.22162.

• J. Gronsbell, J. Minnier, S. Yu, K. Liao, and T. Cai, 2019 “Automated Feature Selection of Predictors in Electronic Medical Records Data.” Biometrics, doi: 10.1111/biom.12987.

• C. Hong, K. Liao and T. Cai, 2019 “Semi-supervised Validation of Multiple Surrogate Outcomes with Application to Electronic Medical Records Phenotyping.” Biometrics, doi: 10.1111/biom.12971.

• J. Gronsbell and T. Cai, 2018 “Semi-Supervised Approaches to Efficient Evaluation of Model Prediction Performance.” Journal of Royal Statistical Society, Series B, 80(3): 579–94.

• J. Sinnott and T. Cai, 2018 “Pathway aggregation for survival prediction via multiple kernel learning.” Statistics in Medicine, 37(16), 2501-2515.

• Zhou QM, Dai W, Zheng Y, Cai T, 2017 “Robust Dynamic Risk Prediction with Longitudinal Studies.” Stat Theory Related Fields; 1(2):159-170.

• D. Agniel, T. Cai, 2017 “Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis.” Biometrics, 73(4): 1254-1265.

• W. Dai, M. Yang, C. Wang, and T. Cai, 2017 “Sequence Robust Association Test (SRAT) for Familial Data.” Biometrics, 73 (3), 876-84.

• Y. Zheng, M. Brown, A. Lok, T. Cai, 2017 “Improving Efficiency in Biomarker Incremental Value Evaluation under Two-phase Designs”. Annals of Applied Statistics, 11 (2), 638-54.

• R. Payne, M. Yang, Y. Zheng, M. K. Jensen, T. Cai, 2016 “Robust Risk Prediction with Biomarkers under Two-Phase Stratified Cohort Design”. Biometrics, 72(4):1037–1045.

• D. Agniel, K.P. Liao, T. Cai, 2016 “Estimation and testing for multiple regulation of multivariate mixed outcomes.” Biometrics Dec;72(4):1194-1205.

• F. Yang, L. Tian, S. Yu, T. Cai and L.J. Wei, 2016 “Optimal stratification in outcome prediction using baseline information”. Biometrika, 103 (4), 817-28.

• M. Neykov, J. Liu, and T. Cai, 2016 “On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting”. Journal of Machine Learning Research, 17(70), 1-32.

• Y. Shen and T. Cai, 2016 “Identifying Predictive Markers for Personalized Treatment Selection”. Biometrics, 72(4), 1017-1025.

• J. Minnier, M. Yuan, J. Liu and T. Cai, 2015 “Risk Classification with an Adaptive Naive Bayes KernelMachine Model.” Journal of the American Statistical Association, 110(509): 393-404.

• Y. Shen, K. Liao, and T. Cai, 2015 “Sparse Kernel Machine Regression for Ordinal Outcomes”. Biometrics, 71(1):63-70.

• T. Cai and Y. Zheng, 2013, “Resampling Procedures for Making Inference under Nested Case-control Studies”. Journal of the American Statistical Association, 108, 1532–1544.

• J. Sinnott and T. Cai, 2013, “Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling”. Biometrics, 69(4): 861-73.

• L. Parast and T. Cai, 2013 “Landmark risk prediction of residual life for breast cancer survival”. Statistics in Medicine, 32(20): 3459-71.

• L. Parast, S. Cheng and T. Cai, 2012 “Landmark Prediction of Long Term Survival Incorporating Short Term Event Time Information.” Journal of the American Statistical Association, 107, 1492-1501.

• T. Cai, Y. Zheng, 2011 “Nonparametric Evaluation of Biomarker Accuracy Under Nested Case-Control Studies”. Journal of the American Statistical Association, 106, 569-580.