Indiana University
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Changyu Shen

EDUCATION

University of Science and Technology of China, Hefei, AnHui Province, P. R. China, B.S. (Biology), 1998

University of Pittsburgh, Pittsburgh, PA, USA, Ph.D. (Biostatistics), 2004                                                     

ACADEMIC APPOINTMENTS

2004-2010            Assistant Professor, Division of Biostatistics, Indiana University School of   Medicine

2010-2011            Associate Professor, Division of Biostatistics, Indiana University School of Medicine

2011-                     Associate Professor, Department of Biostatistics, Indiana University School of  Medicine

SELECTED HONOR AND NATIONAL SERVICE

2004     Outstanding Student Award, Graduate School of Public Health, University of Pittsburgh

2008-    Associate Editor, Heart Rhythm

My primary research focus is in the inference of the effects of interventions/exposures in populations and sub-populations from observational and randomized studies. The statistical issues I am interested in include causal inference based on observational data, analysis of incomplete data, causal inference in sub-populations, prediction of causal effect, and empirical/full Bayesian approaches. In addition, I am also interested in statistical modelling of OMICS data and  identifying genetic/epigenetic/proteomic/metabolomic markers to improve intervention strategies. A general scheme of these research activies is to personalize intervention based on each individual's unique characteristics.

My collaborative research is mainly in cardiology and cancer.

Selected Publications (*: student or post-doc advised)

Missing Data and Causal Inference

  1. Shen C, Weissfeld L.  Application of pattern-mixture models to outcomes that are potentially missing not at random using pseudo maximum likelihood estimation. Biostatistics 2005;6:333-347.
  2. Shen, C. and Weissfeld, L.   A copula model for repeated measurement with non-ignorable non-monotone missing outcome. Statistics in Medicine 2006;25:2427-2440.
  3. Shen, C.  Application of multiple imputation to data from two-phase sampling: estimation of the incidence rate of cognitive impairment. Journal of Data Science 2007; 5: 503-518.
  4. Shen, C. and Gao, S.  A Mixed-effects Model for Cognitive Decline with Non-monotone Non-response from a Two-phase Longitudinal Study of Dementia. Statistics in Medicine 2007; 26:409-425.
  5. Dodge, H. H., Shen, C., Ganguli, M. Practical application of pattern-mixture and latent trajectory modeling to access longitudinal cognitive decline under conditions of non-ignorable missingness. Journal of Data Science 2008; 6: 231-246. 
  6. Qin, L., Weissfeld L., Shen, C., Levine, M. D. A. Two-latent-class Model for Smoking Cessation Data with Informative Dropouts. Communications in Statistics-Theory and Methods 2009; 38: 2604-2619.
  7. Shen, C., Li, X., Li, L., and Were, M. C.  Sensitivity analysis for causal inference using inverse probability weighting. Biometrical Journal 2011; 53: 822-837.
  8. Li, L., Shen, C., Wu, A. C., and Li, X. Propensity score-based sensitivity analysis method for uncontrolled confounding. American Journal of Epidemiology 2011; 174: 345-353.
  9. Li, L., Shen, C., Li, X., and Robins, J. M. On weighting approaches for missing data. Statistical
    Methods in Medical Research
    2013; 22: 14-30.
  10. Li, X., Shen, C. Linkage of patients records from disparate sources. Statistical Methods in Medical Research 2013; 22: 31-38.
  11. Shen C, Li, X, Li, L.  Inverse probability weighting for covariate adjustment in randomized studies. Statistics in Medicine (in press)

Personalized Medicine

  1. Shen, C., Jeong, J.*, Li, X., Chen, P. S., and Buxton, A. E. Treatment benefit and treatment harm rate to characterize heterogeneity in treatment effect. Biometrics (in press).

Proteomics and Melabolomics

  1. Chen, J. Y., Shen. C. and Sivachenko, A. Y. Mining Alzheimer Disease Targets from Integrated Protein Interactome Data.  Proceedings of 2006 Pacific Symposium of Biocomputing, 367-378.
  2. Chen, J. Y., Wang, M. and Shen, C. An Integrated Computational Proteomics Method to Extract Protein Targets for Fanconi Anemia Studies. Proceedings of 2006 Symposium on Applied Computing, 173-179.
  3. Shen, C., Li, L. and Chen, J. Y.  A statistical framework to discover true association from multi-protein complex pull-down proteomics data sets, Proteins: structure, function, and bioinformatics 2006; 64:436-443. 
  4. Shen, C., Breen, T.E., Dobrolecki, L.E., Schmidt, C.M., Sledge, G.W., Miller, K.D. and Hickey, R. J. Comparison of Computational Algorithms for the Classification of Liver Cancer using SELDI Mass Spectrometry: A Case Study.  Cancer Informatics 2007; 3: 339-349.
  5. Chen, J. Y., Yan, Z., Shen, C., and Wang, Mu. A Systems Biology Case Study of Ovarian Cancer Drug Resistance. Journal of Bioinformatics and Computational Biology 2007;5: 383-405. 
  6. Shen, C., Wang, Z, Shankar, G, Zhang, X, Li, L. A Hierarchical Statistical Model to Assess the Confidence of Peptides and Proteins Inferred from Tandem Mass Spectrometry. Bioinformatics 2008;24: 202-208.
  7. Saha, S., Harrison, S. H., Shen, C., Tang, H. Radivojac, P., Arnold, R. J., Zhang, X., and Chen, J. Y. HIP2: An Online Database of Human Plasma Proteins from Healthy Individuals. BMC Medical Genomics 2008; 1:12.
  8. Shen, C., Sheng, Q., Dai, J., Li, Yi., Zeng, R., and Tang, H. On the estimation of false positives in peptide identifications using decoy search strategy. Proteomics 2009; 9: 194-204.
  9. Jeong, J.*, Shi, Xue, Zhang, X., Shen, C. An empirical Bayes model using a competition score
    for metabolite identification in gas chromatography mass spectrometry. BMC Bioinformatics
    2011; 12: 392.
  10. Jeong, J.*, Shi, X., Zhang, X., Kim, S., Shen, C. Model-based peak alignment of metabolomics profiling from comprehensive two-dimensional gas chromatography mass spectrometry. BMC Bioinformatics 2012; 13: 27.
  11. Jeong, J.*, Zhang, X., Shi, X., Kim, S., and Shen, C. An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS. BMC Bioinformatics (in press)

Genomics and Epigenomics

  1. Wang, X., Wang, G., Shen, C., Li, L., Wang, X. Edenberg, H.J., Sanford, J., Liu, Y. Refining Detection of Protein Binding Regions Using Pyrosequencing-derived RNA Fragments. BMC Genomics 2008; 9 Suppl 1: S17.
  2. Li, L., Borges, S., Robarge, J. D., Shen, C., Mooney, S., Desta, Z., Flockhart, D. A Mixture Model Approach in
    Gene-Gene and Gene-Environmental Interactions for Binary Phenotypes.  Journal of Biopharmaceutical Statistics 2008; 18: 1150-1177.
  3. Li, L., Yu, M., Robarge, J. D., Shen, C., Gao, S., Jin, Y., Borges-Gonzales, S., Nguyen, A., Todd, S.,  Desta, Z., McLeod, H. L., Sweeney, C. J., and Flockhart, D. A. A Penalized mixture model approach in Phenotype/phenotype association analysis for quantitative phenotype. Cancer Informatics 2010; 9: 93-103.
  4.  Wang, G., Wang Y., Shen, C., Huang Y., Huang, K., Huang, T. H-M., Nephew, K.P., Li, L., and Liu, Y. RNA Polymerase II binding patterns reveal genomic regions involved in microRNA gene regulation. PLoS ONE 2010; 5(11): e13798
  5. Jeong, J.*, Li, L., Liu, Y., Nephew, K. P., Huang, T-H., Shen, C. An Empirical Bayes Model for Gene Expression and Methy-lation Pro files in Antiestrogen Resistant Breast Cancer. BMC Medical Genomics 2010;3:55. 
  6. Shen, C., Huang, Y., Liu, Y., Wang, G., Zhao, Y., Wang, Z., Teng, M., Wang, Y., Flockhart, D. A., Skaar, T. C., Yan, P., Nephew, K., Huang, T., and Li, L. A modulated empirical Bayes model for identifying topological and temporal estrogen receptor alpha regulatory networks in breast cancer BMC System Biology 2011; 5: 67. 
  7. Teng, M., Wang, Y., Kim, S., Li, L., Shen, C., Wang, G., Liu, Y., Huang, T.H.-M., Nephew, K. P., and Balch, C. Empirical Bayes model comparisons for differential methylation analysis. Comparative and Functional Genomics 2012; doi:10.1155/2012/376706

Collaborations 

  1. Dodge HH, Shen C, Pandav R, DeKosky ST, Ganguli M.  Functional transitions and active life expectancy associated with Alzheimer’s disease. Archives of Neurology 2003; 60:253-9.
  2. Ganguli M, Dodge HH, Shen C, DeKosky ST. Mild cognitive impairment, amnestic type: an epidemiologic study. Neurology 2004; 63:115-121.
  3. Bharucha AJ, Pandav R, Shen C, Dodge HH, Ganguli M. Predictors of institutionalization: A
    12-year epidemiological study in the USA. Journal of American Geriatric Society 2004; 52: 434-9.
  4.  Ganguli M, Dodge HH, Shen C, Pandav RS, DeKosky ST. Alzheimer disease and mortality: a
    15-year epidemiologic study. Archives of Neurology 2005; 62:779-784.
  5. Ganguli, M., Vander Bilt, J., Saxton, J.A., Shen, C., Dodge, H.H.  Alcohol consumption and
    cognitive function in late life: a longitudinal community study. Neurology 2005; 65:1210-1217.
  6. Kirkman, M. S., Shankar, R. R., Shankar, S., Shen, C., Brizendine, E., Baron, A. and McGil, J. Treating postprandial hyperglycemia does not appear to delay progression of early type 2 diabetes: the Early Diabetes Intervention Program. Diabetes Care, 2006; 29: 2095-2101.
  7. Carroll, A., Ackermann, R., Brizendine, E., Shen, C., Marrero, D. Does age of diagnosis
    influence long-term physical and behavioral outcomes? Diabetes Care 2007; 30: 2859-2860.  
  8. Das, M. K., Suradi, H., Maskoun, W., Michael, M. A., Shen, C., Peng, J., Dandamudi, G., and Mahenthiran, J. Fragmented Wide QRS on a 12-Lead ECG: A Sign of Myocardial Scar and Poor Prognosis.  Circulation 2008; 1: 258-268.
  9. Dube, M. P., Shen, C., Greenwald M., and Mather, K. J. No impairment of endothelial function or insulin sensitivity with four weeks of the HIV protease inhibitors Atazanavir or Lopinavir-Ritonavir in healthy HIV-uninfected subjects: a placebo-controlled trial.  Clinical Infectious Disease 2008; 47: 567-574.
  10.  Shao, M., Cao, L., Shen, C., Satpathy, M., Chelladurai, B., Bigsby, R., Nakshatri, H., Matei, D.
    Epithelial-to-Mesenchymal Transition and Ovarian Tumor Progression Induced by Tissue Transglutaminase. Cancer Research 2009; 69: 9192-9201.
  11. Das, M. K., Michael, M. A., Suradi, H., Peng, J., Sinha, A., Shen, C., Mahenthiran J., and Kovacs, R. J. Usefulness of Fragmented QRS on a 12-lead Electrocardiogram in Acute Coronary Syndrome for
    Predicting Mortality. Journal of American College of Cardiology 2009; 104: 1631-1637.
  12. Dube, M. P., Shen, C., Mather, K. J., Waltz, J., Greenwald, M., and Gupta, S. K. Relationship of  body composition, metabolic status, antiretroviral use, and HIV disease factors to endothelial dysfunction in HIV-infected subjects. AIDS Research and Human Retroviruses 2010; 26: 847-854. 
  13. Shen, M. J., Shinohara, T., Park, H., Frick, K., Ice, D. S., Choi, E., Han, S., Maruyama, M., Sharma, R., Shen, C., Fishbein, M., Chen, L. S., Lopshire, J. C., Zipes, D. P., Lin, S., and Chen, P.S. Continuous Low-Level Vagus Nerve Stimulation Reduces Stellate Ganglion Nerve Activity and Paroxysmal Atrial Tachyarrhythmias in Bmbulatory Canines. Circulation 2011; 123: 2204-2212. 
  14. Han, S., Kobayashi, K., Joung, B., Piccirillo, G., Maruyama, M., Vinters, H. V., Match, K., Lin, S-F., Shen, C., Fishbein, M. C., and Chen, L. S.  Electroanatomic Remodeling of the Left Stellate
    Ganglion After Myocardial Infarction. Journal of the American College of Cardiology 2012; 59: 954-961.
  15. Matei, D., Fang, F., Shen, C., Schilder, J., Arnold, J., Zeng, Y., Berry, W. A., Huang, T., Nephew, K. P. Epigenetic Resensitization to Platinum in Ovarian Cancer. Cancer Research 2012; 72: 2197-2205.

 

 

  

BIOS546 Applied Longitudinal Data Analysis

BIOS646 Advanced Generalized Linear Models

BIOS621 Advanced Statistical Computing