Brian Neelon, PhD
Associate Professor
Department of Public Health Sciences
Medical University of South Carolina
Contact Information:
Room CS305K
Department of Public Health Sciences
Medical University of South Carolina
135 Cannon Street, Suite 303
Charleston, SC 29425-8350 USA
E-mail: neelon@musc.edu
Research Interests: Bayesian inference; longitudinal data analysis; latent growth models; zero-inflated and semicontinuous models; spatial data analysis; health services research
Job Announcement: I have an opening for a Senior Research Associate to work on the NICHD Fetal Growth ECHO Project. The applicant will conduct data analyses in SAS/R to examine associations between fetal growth and later childhood development. There are also opportunities to develop new statistical methodology. Applicants should have experience with Bayesian hierarchical models and longitudinal data analysis. For more information or to apply online, please see this link.
Somewhat Current CV
Selected Publications
- Walker RJ, Neelon B, Davis ML, Egede LE (2018). Racial differences in spatial patterns for poor
glycemic control in the southeastern United States. Annals of Epidemiology, 28, 153-159.
- Davis ML, Neelon B, Nietert PJ, Hunt KJ, Burgette LF, Lawson AB, Egede LE (2017). Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes. Statistical Methods in Medical Research.
In press.
- Smith VA, Neelon B, Maciejewski ML, Preisser JS (2017). Two parts are better than one: Modeling marginal means of semicontinuous data. Health Services and Outcomes Research Methodology, 17, 198-218.
- Benecha H, Neelon B, Divaris K, Preisser JS (2017). Marginalized mixture models for count data from multiple source populations. Journal of Statistical Distributions and Applications, 4, 3.
- Smith VA, Neelon B, Preisser JS, Maciejewski ML (2017). A marginalized two-part model for longitudinal semicontinuous data. Statistical Methods in Medical Research, 26, 1949-1968.
- Neelon B and Chung D (2017). The LZIP: A Bayesian latent factor model for correlated zero-inflated counts. Biometrics, 73, 185-196.
- Neelon B, O'Malley AJ, Smith VA (2016). Modeling zero-modified count and semicontinuous data in health services research, part 1: Background and overview. Statistics in Medicine, 35, 5070-5093.
- Neelon B, O'Malley AJ, Smith VA (2016). Modeling zero-modified count and semicontinuous data in health services research, part 2: Case studies. Statistics in Medicine, 35, 5094-511.
- Neelon B, Li F, Burgette LF, Benjamin Neelon SE (2015). A spatiotemporal quantile regression
model for emergency department expenditures. Statistics in Medicine, 34, 2559-2575.
- Zhao L, Feng D, Buyse M, Neelon B (2015).
Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers. Statistics in Medicine, 34, 1733-1746.
- Neelon B, Zhu L, Benjamin Neelon SE (2015). Bayesian two-part spatial models for semicontinuous data with application to emergency department expenditures. Biostatistics, 16, 465-479.
- Neelon B, Chang HC, Ling Q, Hastings SN (2014). Spatiotemporal hurdle models for zero-inflated count data: exploring trends in emergency department visits. Statistical Methods in Medical Research. In press.
- Here's R2WinBUGS code for fitting the various models presented in the paper.
- Amatya A, Bhaumik D, Normand S-L, Greenhouse J, Kaizar E, Neelon B, Gibbons RD (2014). Likelihood-based random effect meta-analysis of binary events. Journal of Biopharmaceutical Statistics, 25, 984-1004.
- Smith VA, Preisser JS, Neelon B, Maciejewski ML (2014).
A marginalized two-part model for semicontinuous data. Statistics in Medicine, 33, 4891-4903.
- Neelon B, Gelfand AE, Miranda ML (2014). A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores. Journal of the Royal Statistical Society: Series C, 6, 737-761.
- Here's R code for fitting a simulated example.
- Neelon B, Anthopolos R, Miranda ML (2014). A spatial bivariate probit model for correlated binary data with application to adverse birth outcomes. Statistical Methods in Medical Research, 23(2), 119-133.
- Here's R code for fitting a simulated example.
- Neelon B, Ghosh P, Loebs PF (2013). A spatial Poisson hurdle model for exploring geographic variation in emergency department visits. Journal of the Royal Statistical Society: Series A, 176(2), 389-413.
- Montagna S, Tokdar ST, Neelon B, Dunson DB (2012). Bayesian latent factor regression for functional and longitudinal data. Biometrics, 68, 1064-1073.
- Bhaumik DK, Amatya A, Normand S-L, Greenhouse J, Kaizar L, Neelon B, Gibbons RD (2012). Meta-analysis of binary rare adverse event data. Journal of the American Statistical Association, 107, 555-567.
- Neelon B, Swamy GK, Burgette LF, and Miranda ML (2011).
A Bayesian growth mixture model to examine maternal hypertension and birth outcomes. Statistics in Medicine, 30, 2721-2735.
- Here's R code for fitting a simulated example with random intercepts.
- Neelon B, O'Malley AJ, and Normand S-L (2011).
A Bayesian two-part latent class model for longitudinal medical expenditure data: assessing the impact of mental health and substance abuse parity. Biometrics, 67, 280-289.
- Neelon B and O'Malley AJ. (2010). Bayesian analysis using power priors with application
to pediatric quality of care. Journal of Biometrics and Biostatistics, 1:103. doi: 10.4172/2155-6180.1000103
- Neelon B, O'Malley AJ, and Normand S-L (2010).
A Bayesian model for repeated measures zero-inflated count data with application to psychiatric outpatient service use. Statistical Modelling, 10(4):421-439.
- Here's WinBUGS code for fitting
the zero-inflated Poisson Model. Here's code for fitting the zero-altered and hurdle models.
- Neelon B and Dunson DB (2004).
Bayesian isotonic regression and trend analysis.
Biometrics, 60, 177-191.
- Dunson DB and Neelon B
(2003). Bayesian inference on order constrained parameters in
generalized linear models. Biometrics, 59, 286-295.
- R code is available here. This ZIP file contains code for the minmax transformation function, along with a linear regression example illustrating the method.
Books and Book Chapters
- Neelon B (2016). Spatial data analysis for health services research. In Andrew Lawson, Sudipto Banerjee, Robert Haining and Lola Ugarte (eds.) Handbook of Spatial Epidemiology. Chapter 29, pages 549--562. CRC Press.
- Kupper L, Neelon B, O'Brien S. (2013). Exercises and Solutions in Statistical Theory. CRC Press.
- Kupper L, Neelon B, O'Brien S. (2010). Exercises and Solutions in Biostatistical Theory. CRC Press.