John S. Preisser, PhD

(919) 966-7265

John_Preisser@unc.edu

3105-F McGavran-Greenberg Hall

CB #7420

Chapel Hill, NC 27599

USA

Curriculum Vitae Personal Website

About

John Preisser, PhD, is a research professor in the Department of Biostatistics at the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill (UNC) and deputy director of the biostatistics service with the North Carolina Translational & Clinical Sciences Institute (NC TraCS) where he directs the Carolina Pragmatic Trials Laboratory. He is a research fellow in the program on Aging, Disability, and Long-Term Care in the UNC Cecil G. Sheps Center for Health Services Research where he collaborates with experts in the field to conduct research that improves the care of the elderly in nursing homes and residential care facilities. Dr. Preisser is also deeply engaged in oral health research in the UNC School of Dentistry. These vibrant, multi-disciplinary, collaborative research environments motivate him to develop new statistical models and methods, and to promote state-of-art statistical analysis techniques that improve human health and welfare.

Honors and Awards

Elected Fellow
2010, American Statistical Association

Elected Member
2006, International Statistical Institute

Research Fellow of the Cecil G. Sheps Center for Health Services Research
2006-Present, University of North Carolina at Chapel Hill

James E. Grizzle Distinguished Alumnus Award
2001, University of North Carolina at Chapel Hill

Representative Courses

Models and Methodology in Categorical Data (BIOS 765), 2006-2020 (even years)

Research Activities

Dr. Preisser works on interdisciplinary research teams with clinical trialists and implementation scientists to study the impact of interventions on behavioral and health outcomes in real world settings. He currently serves as the biostatistician on a cluster-randomized trial (CRT) that uses a stepped wedge design to evaluate an intervention to improve the care of patients during transitions from skilled nursing facilities to home. He has helped devise stepped wedge and cluster-crossover designs in communities, emergency departments, hospitals and prisons for interventions targeting the opioid epidemic. He has also served as biostatistician on many intervention studies using more traditional CRT designs including a match-pairs CRT in nursing homes that improves oral health to reduce pneumonia rates and a non-randomized repeated cross-sectional CRT to combat underage drinking in communities.

Dr. Preisser’s methodological interests include modeling clustered binary data in a number of settings. First, the use of estimating equations with finite-sample corrections for population-averaged models has application for the analysis of CRTs, which typically have a small number of clusters (e.g., 8-30). In such settings, finite-sample bias corrections are needed to reduce the bias in the estimation of intervention effects. The development of methods for sample size estimation and statistical analysis of stepped wedge and other CRT designs provides an integrated approach to their design and analysis.

Second, Dr. Preisser is interested in health sciences applications involving partial-cluster sampling of correlated binary outcomes from anatomical regions where multi-stage sampling is employed for disease surveillance in populations. For example, large epidemiological investigations such as NHANES may employ partial-mouth oral exams where a subset of tooth surfaces are sampled (in the final stage following sampling of individuals) to estimate person-level disease prevalence for conditions such as chronic periodontitis that have full-mouth definitions. While the logistical and cost-saving motivations for such sampling strategies are strong, a correlated binary data model for the pattern of disease within the mouth is recommended to eliminate or mitigate the bias of prevalence estimates.

Finally, Dr. Preisser is interested in marginalized two-part models for semi-continuous and count outcomes with excess zeros. The general approach is to model the marginal mean of the outcome (i.e., of all the data) instead of the mean of an unobservable subset (latent class) of individuals. Applications include the estimation of overall effects in marginalized models for dental caries counts and medical costs where the two parts correspond to models for the marginal mean and the excess zeros. While interpretations are similar to those of traditional one-part generalized linear models, simulations show that “two parts are better than one” when zeros comprise greater than twenty percent of the data.

Service Activities

2022-present Serve on Data Safety and Monitoring Board for an NIA-funded study

2020-2023 Serve on Data Safety and Monitoring Board for a PCORI-funded study

2019-2022 Patient Centered Outcomes Research Institute Merit Reviewer of Funding Award Proposals

2018-2022 Serve on Data Safety and Monitoring Board for an NIDCR-funded study

2011-present Chair, Pranab K. Sen Distinguished Visiting Professorship Committee, Department of Biostatistics, UNC-Chapel Hill

Key Publications

Multiple Imputation for Partial Recording Periodontal Examination Protocols. Preisser JS, Shing T, Qaqish B, Divaris K, Beck J (2024). JDR Clinical and Translational Research, 9(1), 52-60.

%CRTFASTGEEPWR: A SAS macro for power of generalized estimating equations analysis of multi-period cluster randomized trials with application to stepped wedge designs. Zhang Y, Preisser JS, Li F, Turner EL, Rathouz PJ (2024). Journal of Statistical Software, Code Snippets, 108(1), 1-27.
View publication

GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials. Zhang Y, Preisser JS, Li F, Turner EL, Toles M, Rathouz PJ (2023). Computer Methods and Programs in Biomedicine,, 230(107362), Epub 2023 Jan 20.

A general method for calculating power for GEE analysis of complete and incomplete stepped wedge cluster randomized trials. Zhang Y, Preisser JS, Turner EL, Rathouz PJ, Toles M, Li F (2023). Statistical Methods in Medical Research, 32(1), 71-87.

Education

PhD, Biostatistics, University of North Carolina at Chapel Hill, 1995
MA, Statistics, Pennsylvania State University, 1988
BS, Statistics, Virginia Tech, 1986

John S. Preisser, PhD