Publications<\/strong>
\nShappell, H., Tripodis, Y., Killiany, R., Kolaczyk, E.D., (2019). A Paradigm for Longitudinal Complex Network Analysis over Patient Cohorts in Neuroscience. Network Science.<\/p>\nShappell, H., Caffo, B. S., Pekar, J. J., Lindquist, M. A. (2019). Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models. NeuroImage, 191, 243-257.<\/p>\n
Gordon, L. B., Shappell, H., Massaro, J., D\u2019Agostino, R. B., Brazier, J., Campbell, S. E., … & Kieran, M. W. (2018). Association of lonafarnib treatment vs no treatment with mortality rate in patients with Hutchinson-Gilford progeria syndrome. Jama, 319(16), 1687-1695.<\/p>\n
Russinova, Z., Bloch, P., Wewiorski, N., Shappell, H., Rogers, E. S. (2018). Predictors of sustained employment among individuals with serious mental illness: findings from a 5-year naturalistic longitudinal study. The Journal of nervous and mental disease, 206(9), 669-679.<\/p>\n
Maru, M., Rogers, E. S., Hutchinson, D., Shappell, H. (2018). An Integrated Supported Employment and Education Model: Exploratory Study of an Innovative Approach Designed to Better Meet the Needs of Young Adults with Psychiatric Conditions. The journal of behavioral health services & research, 45(3), 489-498.<\/p>\n
Burke, G. M., Genuardi, M., Shappell, H., D’Agostino Sr, R. B., & Magnani, J. W. (2017). Temporal associations between smoking and cardiovascular disease, 1971 to 2006 (from the Framingham Heart Study). The American journal of cardiology, 120(10), 1787-1791.<\/p>\n
Godbole, H., Grzesik, K., Shappell, H., (2017). Poisson Approximations for the Number of kl-Scans. Handbook of Scan Statistics, 1-8.<\/p>\n
Gordon, L.B., Kleinman, M.E., Massaro, J.M., D’Agostino, R.B., Shappell, H., Gerhard-Herman, M., Smoot, L.B., Gordon, C.M., Cleveland, R.H., Nazarian, A. and Snyder, B.D., (2016). Clinical Trial of Protein Farnesylation Inhibitors Lonafarnib, Pravastatin and Zoledronic Acid in Children with Hutchinson-Gilford Progeria Syndrome. Circulation, pp.CIRCULATIONAHA-116.<\/p>\n
Vathipadiekal, V., Farrell, J., Shuai, Z., Edward, H., Shappell, H., Al-Rubaish, A.M., Al-Muhanna, F., Naserullah,Z.,Alsuliman, A.,Simkin, I. and Farrer, L., (2015). A Candidate Trans-Acting Modulator of Fetal Hemoglobin Gene Expression in the Arab-Indian Haplotype of Sickle Cell Anemia. Blood,126(23), pp.409-409.<\/p>\n
Sebastiani, P., Farrell, J.J., Alsultan, A., Wang, S., Edward, H.L., Shappell, H., Bae, H., Milton, J.N., Baldwin, C.T., Al-Rubaish, A.M. and Naserullah, Z., (2015). BCL11A enhancer haplotypes and fetal hemoglobin in sickle cell anemia. Blood Cells, Molecules, and Diseases, 54(3), pp.224-230.<\/p>\n
Kaplan, W., Sharma, Abhishek., Shappell, H., Kolaczyk, E.D., (2016). Insulin Trade Profile Technical Report. Health Action International.<\/p>\n
Papers under Review<\/strong><\/p>\nShappell, H., Kramer, M., Chu, C., Kolaczyk, E.D., Accounting for Edge Uncertainty in Stochastic Actor Oriented Models for Dynamic Network Analysis. Under review at Journal of Computational and Graphical Statistics.<\/p>\n
Invited Talks<\/strong><\/p>\n2018 Methods for Longitudinal Complex Network Analysis in Neuroscience. Mathematics and Statistics Department at East Tennessee State University, Johnson City, TN.<\/p>\n
2018 Methods for Longitudinal Complex Network Analysis in Neuroscience. Statistical Methods in Imaging Conference, Philadelphia, PA.<\/p>\n
2018 Methods for Longitudinal Complex Network Analysis in Neuroscience. Eastern North American Region Spring Meeting, Atlanta, Georgia.<\/p>\n
2017 Accounting for Uncertainty in Stochastic Actor Oriented Models, International Conference on Computational and Methodological Statistics, London, England.<\/p>\n
2017 Accounting for Uncertainty in Stochastic Actor Oriented Models, New England Statistics Symposium, University of Connecticut.<\/p>\n
2016 Dynamic Network Analysis in Resting-State fMRI for Alzheimer\u2019s Disease, Joint Statistics Meeting, Chicago, IL.<\/p>\n
Contributed Talks<\/strong><\/p>\n2019 A Simulation-Based Comparison of Dynamic Connectivity Methods in fMRI, Joint Statistics Meeting, Denver, CO.<\/p>\n
2018 Likelihood Based Dynamic Connectivity Analysis using Hidden Semi-Markov Models, Joint Statistics Meeting, Vancouver, British Columbia.<\/p>\n
2015 Dynamic Network Analysis in Resting-State fMRI for Alzheimer\u2019s Disease, Joint Statistics Meeting, Seattle, WA.<\/p>\n
2011 Planarized Pascal\u2019s Triangle mod a general prime p graphs and their properties, Joint
\nMathematics Meeting, New Orleans, LA.<\/p>\n
2010 Sierpinski Gasket Graphs mod p, Southeastern REU Minisymposium, UNC, Ashville.<\/p>\n
Posters<\/strong><\/p>\n2019 A Simulation-Based Comparison of Dynamic Connectivity Methods, Organization for Human Brain Mapping Conference, Rome, Italy.<\/p>\n
2018 Likelihood Based Dynamic Connectivity Analysis using Hidden Semi-Markov Models, Organization for Human Brain Mapping Conference, Singapore.<\/p>\n
2016 A Paradigm for Longitudinal Network Analysis over Patient Cohorts in Neuroscience, Workshop hosted by Columbia University Department of Statistics.<\/p>\n
2016 A Paradigm for Longitudinal Network Analysis over Patient Cohorts in Neuroscience, New England Statistics Symposium, Yale University.<\/p>\n
2016 Update on Impact of Farnesylation Inhibitors on Survival in Hutchinson-Gilford Progeria Syndrome, Progeria Research Foundation 8th International Scientific Workshop, Boston, MA.<\/p>\n
2016 A Paradigm for Longitudinal Network Analysis over Patient Cohorts in Neuroscience, Boston University\/ Keio University Probability and Statistics Workshop, Boston, MA.
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