Jing Cao, Associate Professor

 

Contact

 

Department of Statistical Science             
Southern Methodist University                 

3225 Daniel Avenue Avenue         

P O Box 750332                             

Dallas, Texas 75275-0332

Phone:    214-768-2451  Fax:  214-768-4035

jcao@smu.edu

 

 

  Education

 

  Ph.D., Statistics, University of Missouri, Columbia, 2005

 


Research Interests

 

  • Bayesian methodologies and applications  
  • High-throughput data analysis
  • Ordinal data analysis
  • Item response theory
  • Sample size determination in clinical trials

Teaching

 

  • STAT 2301 - Business Statistics  
  • STAT 6345 - Linear Regression
  • STAT 6395 – Bayesian Hierarchical Modeling

Selected Publications

       ·  Cao, J. and Stokes, S.L. (2008).  Bayesian IRT guessing models for partial guessing behaviors. Psychometrika, 73, 209-230.

       ·  Cao, J., He, C., and McCoy, T. (2008).  Bayesian estimation of age-specific bird nest survival rates with categorical covariates. Environmental and Ecological Statistics, 15, 49-58.

       ·  Cao, J., Xie, X., Zhang, S., Whitehurst, A., and White, M. (2009). Bayesian optimal discovery procedure for simultaneous significance testing. BMC Bioinformatics, 10:5.

       ·  Zhang, S and Cao, J. (2009). A close examination of double filtering with fold change and t test in microarray analysis. BMC Bioinformatics, 10:402.  

       ·  Livingston, E.H., Elliot, A.C., Hynan, L.S., and Cao, J. (2009). Effect size estimation: necessary component of statistical analysis. Archives of Surgery, 144, 706-712.

       ·  Cao, J., Lee, J.J., and Albert, S. (2009). Comparison of Bayesian sample size determination criteria: ACC, ALC, and WOC. Journal of Statistical Planning and Inference, 139, 4111-4122.

       ·  Cao, J., He, C., Suedkamp Wells, K.M., Millspaugh, J.J., and Ryan, M.R. (2009).  Modeling age and nest-specific survival using a hierarchical Bayesian approach. Biometrics, 65, 1052-1062.

       ·  Zhang, S., Cao, J., Kong, M., and Scheuermann, R.H. (2010). GO-Bayes: Gene Ontology-based over-representation analysis using a Bayesian approach. Bioinformatics, 26, 905-911.

       ·  Cao, J., Moosman, A., and Johnson, V.E. (2010). A Bayesian goodness-of-fit test for censored data. Biometric, 66, 426-434.

       ·  Cao, J., Stokes, S.L., and Zhang, S. (2010). A Bayesian approach to ranking and rater evaluation: an application to grant reviews. Journal of Educational and Behavioral Statistics, 35, 194-214.

       ·  Cao, J. and Stokes, S.L. (2010).  Evaluation of wine judge performance through three characteristics: bias, discrimination, and variation. Journal of Wine Economics, 5(2), 1-11.

       ·  Cao, J. and Zhang, S. (2010).  Measuring statistical significance for full Bayesian methods in microarray analysis. Bayesian Analysis, 5, 413-428.

       ·  Livingston, E.H. and Cao, J. (2010). Procedure volume as a predictor of surgical outcomes. Journal of the American Medical Association, 304, 95-97.

       ·  Zhang, S., Cao, J., and Chul, A. (2010).  Calculating sample size in trials using historical controls. Clinical Trials, 7, 343-353.

       ·  Livingston, E.H., Cao, J., and Dimick, J.B. (2010).  Tread carefully with stepwise regression. Archives of Surgery, 145, 1039-1040.

       ·  Cao, J. (2011). Unbiased FDR for full Bayesian methods in microarray analysis. Twenty Seventy Southern Biomedical Engineering Conference Proceedings, 5:2.

       ·  Zhang, S., Cao, J., and Ahn, C. (2013). Sample size calculation for studies comparing binary outcomes using historical controls. Biometrical Journal, 55, 190-202.

       ·  Hodgson, R. and Cao, J. (2014). Criteria for accrediting expert wine judges. Journal of Wine Economics, 9(1), 62-74.

       ·  Zhang, S., Cao, J., and Ahn, C. (2014). A GEE Approach to Determine Sample Size for Pre- and Post-Intervention Experiments with Dropout, Computational Statistics and Data Analysis, 69, 114-121.

       ·  Cao, J. and Zhang, S. (2014). A Bayesian extension of the hypergeometric test for functional enrichment analysis. Biometrics, 70(1), 84-94.

       ·  Cao, J. (2014). Quantifying randomness versus consensus among wine quality ratings. Journal of Wine Economics, 9(2), 202-213.

       ·  Cao, J. and Zhang, S. (2014). Multiple comparison procedures. Journal of the American Medical Association, 312, 543-544.

       ·  Olkin, I., Lou, Y., Stokes, L., and Cao, J. (2014). Analyses of wine-tasting data: a tutorial. Journal of Wine Economics, in press.

       ·  Lou, Y., Cao, J., Zhang, S., and Ahn, C. (2015). Sample size calculations for time-averaged difference of longitudinal binary outcomes. Communications in Statistics – Theory and Methods, accepted.

       ·  Zhang, S., Cao, J., and Ahn, C. (2015). Sample size calculation for before-after experiments with partially overlapping cohorts. Contemporary Clinical Trials, accepted.

       ·  Lou, Y., Cao, J., Zhang, S., and Ahn, C. (2015), Sample size estimation for a two-group comparison of repeated count outcomes using GEE, Communications in Statistics Theory and Methods, in press.

       ·  Zhang, Y., Cao, J., Zhang, S., Lee, A.J., Sun, G., Larsen, C.N., Zhao, H., Gu, Z., He, S., Klem, E.B., and Scheuermann, R.H. (2016), Genetic Changes Found in a Distinct Clade of Enterovirus D68 Associated with Paralysis during the 2014 Outbreak, Virus Evolution, DOI:               http://dx.doi.org/10.1093/ve/vew015

 

 

R program

·         R code to evaluate sample size and empirical power for 2-group comparison of slopes for clinical trials with a count outcome

·         R code to evaluate sample size and empirical power for K-group comparison of slopes for clinical trials with a count outcome

 

 

Doctoral Students Supervised 

        ·  Ayman Ibrahim Al-Rawashdeh, “Semiparametric Bayesian Methods for Multivariate Survival Data”, 2013.

        ·  Ying Lou, “Sample Size Estimation for Comparing Repeated Count Measurements Using GEE”, 2014.

        ·  Yin Xi, “Bayesian Spatial Clustering Method and Its Application in Radiology”, 2015.

        ·  Charles South, “A Dynamic Modelling and Optimization Approach to Daily Fantasy Basketball”, 2016.

 

 


Teaching

All course material is available via Blackboard.