John Preisser, Jr, PhD
My web page for software and data
REACH NC (Collexis) Research Profile
|3105-F McGavran-Greenberg Hall
UNC Gillings School of Global Public Health
135 Dauer Drive
Campus Box 7420
Chapel Hill 27599-7420
|1995||University of North Carolina at Chapel Hill||PhD, Biostatistics|
|1988||Pennsylvania State University||MA, Statistics|
|1986||Virginia Tech||BS, Statistics|
I conduct statistical methods research in the general area of regression analysis for correlated data. I am interested in the analysis of clustered and longitudinal data, methods for count data including zero-inflated Poisson and negative binomial regression models, statistical methods for missing data, and issues pertaining to the design and analysis of cluster intervention trials. My collaborative research activities include aging, particularly observational and interventional studies concerned with the quality of long-term care in nursing homes and residential care settings for those with Alzheimer’s disease and dementia. My principal collaborative research activity is in dentistry including oral epidemiology, biological mechanisms of periodontal disease, dental caries, and craniofacial research. I apply principles of experimental design, clinical trials, epidemiology, health services research, and survey sampling methods.
Long DL, Preisser JS, Herring AH, Golin C. “A Marginalized Zero-Inflated Poisson Regression Model with Overall Exposure Effects.” Statistics in Medicine 2014, 33, 5151-5165.
Perin J, Preisser JS, Qaqish B, Phillips C. “Regression analysis of correlated ordinal data using orthogonalized residuals.” Biometrics 2014, 70, 902-909.
Smith VA, Preisser JS, Neelon B, Maciejewski ML. “A marginalized two-part model for semicontinuous data.” Statistics in Medicine 2014, 33, 4891-4903.
Preisser JS, Das K, Benecha H, Stamm JW. “Logistic regression for dichotomized counts.” Statistical Methods in Medical Research. (published online May 26, 2014). DOI: 10.1177/0962280214536893
Preisser JS, Qaqish BF. “A comparison of methods for simulating correlated binary variables with specified marginal means and correlations.” Journal of Statistical Computation and Simulation 2014; 84, 2441-2452.