Dr. Philip M. Westgate, PhD
Dr. Philip Westgate is an Associate Professor in the Department of Biostatistics. His methodological areas of interest include cluster, group, or community randomized trials, longitudinal studies, and clustered data in general. Furthermore, he has applied interests in a variety of areas, including pediatrics and the substance use area. He is the lead biostatistician for the University of Kentucky site of the HEALing Communities Study (UM1DA049406), and serves as co-investigator on multiple NIH grants. Furthermore, he is part of the University of Kentucky’s Center for Clinical and Translational Science (CCTS) Biostatistics, Epidemiology and Research Design (BERD) Core, and serves on the CCTS data safety monitoring board.
Dr. Westgate’s methodological research focuses on longitudinal studies, cluster, group, or community randomized trials, and clustered data in general. His work has focused on the validity of inference and the power of statistical methods that can be used for the analysis of data in these settings. He also works on study design and the appropriate application of statistical methods. His work is motivated by past and current research studies.
Dr. Westgate is a co-investigator on multiple clinical trials and cluster randomized trials, and is therefore a member on interdisciplinary research teams. For instance, he is the lead biostatistician for the University of Kentucky site of the HEALing Communities Study (UM1DA049406), which is a multi-site cluster randomized trial aimed at reducing opioid-related overdose deaths in communities across four different states. The first citation listed below is work he led on statistical modeling that will be utilized for this study. As another example, he has worked on a clinical trial (R01DA043519) involving neonates with Neonatal Abstinence Syndrome (NAS). Working on NAS-related studies with this research team, he has led the development of a shortened tool for the scoring of NAS severity, and he continues to work with the research team to study potential improvements for NAS severity scoring.
- PhD, Biostatistics
- University of Michigan
- MS, Biostatistics
- University of Michigan
- BS, Statistics and Actuarial Science
- Central Michigan University
- Cluster, group, or community randomized trials
- Clinical trials
- Longitudinal studies
- Clustered data
- Substance use
- Navigation-based interventions
- Westgate, P.M., Cheng, D.M., Feaster, D.J., Fernández, S., Shoben, A.B., Vandergrift, N. (2022). Marginal Modeling in Community Randomized Trials with Rare Events: Utilization of the Negative Binomial Regression Model. Clinical Trials 2022; 19: 162-171.
- Ford, W.P., Westgate, P.M. (2020). Maintaining the Validity of Inference in Small-Sample Stepped Wedge Cluster Randomized Trials with Binary Outcomes when using Generalized Estimating Equations. Statistics in Medicine, 39, 2779-2792.
- Gomez-Pomar, E., Finnegan, L.P., Devlin, L., Bada, H., Concina, V.A., Ibonia, K.T., Westgate, P.M. (2017) Simplification of the Finnegan Neonatal Abstinence Scoring System: Retrospective Study of Two Institutions in the USA. BMJ Open, 7:e016176.
- Westgate, P.M., Burchett, W.W. (2016). Improving Power in Small-Sample Longitudinal Studies when Using Generalized Estimating Equations. Statistics in Medicine, 35, 3733-3744.