Gillings School Directory
John S. Preisser, PhD
John S. Preisser, PhD
Professor
Department of Biostatistics
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 biostatistics collaboration. 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 Fellow2010, American Statistical Association
Elected Member2006, International Statistical Institute
Research Fellow of the Cecil G. Sheps Center for Health Services Research2006-Present, University of North Carolina at Chapel Hill
James E. Grizzle Distinguished Alumnus Award2001, University of North Carolina at Chapel Hill
Representative Courses
Models and Methodology in Categorical Data (BIOS 765), 2006-2018 (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.
Key publications from PCORI funding award ME-2019C1-16196
A general method for calculating power for GEE analysis of complete and incomplete stepped wedge cluster randomized trials. Ying Zhang, John Preisser, Elizabeth Turner, Paul Rathouz, Mark Toles, Fan Li (2022). Statistical Methods in Medical Research. In press.
Generalizing the information content for stepped wedge designs: a marginal modeling approach. Fan Li, Jessica Kasza, Elizabeth Turner, Paul Rathouz, Andrew Forbes, John Preisser (2022). Scandinavian Journal of Statistics. In press.
Impact of unequal cluster sizes for GEE analyses of stepped wedge cluster randomized trials with binary outcomes. Zibo Tian, John Preisser, Denise Esserman, Elizabeth Turner, Paul Rathouz, Fan Li (2022). Biometrical Journal, 64, 419-439.
Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes. Fan Li, Hengshi Yu, Paul Rathouz, Elizabeth Turner, John Preisser (2022). Biostatistics, 23, 772-788.
Service Activities
2020-present 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
Optimal allocation of clusters in cohort stepped wedge designs. Fan Li, Elizabeth Turner, John Preisser (2018). Statistics & Probability Letters, 137(2018), 257-263.
Two parts are better than one: modeling marginal means of semi-continuous data. Valerie Smith, Brian Neelon, Matthew Maciejewski, John Preisser (2017). Health Services Outcomes and Research Methodology, 17(3-4), 198-218.
Matching the statistical model to the research question for dental caries indices with many zero counts. John Preisser, D. Leann Long, John Stamm (2017). Caries Research, 51(3), 198-208.
A new way to estimate disease prevalence from random partial-mouth samples. John Preisser, Sarah Marks, Anne Sanders, Aderonke Akinkugbe, James Beck (2017). Journal of Clinical Periodontology, 44(3), 283-289.
Education
PhD, Biostatistics, University of North Carolina at Chapel Hill, 1995
MA, Statistics, Pennsylvania State University, 1988
BS, Statistics, Virginia Tech, 1986
