I am committed to creating a positive and engaging learning environment for my students. I seek to use innovative strategies to promote active learning, including team-based learning and flipped classrooms.
PHC 4094: Introduction to Biostatistics for Health Science and Public Health
Prereq: STA 2023
Methods and public health applications for analysis of variance, correlation, simple linear regression, multiple linear regression, nonparametric and distribution-free statistical methods, and some basic concepts about survival analysis. Public health applications using statistical software. Writing data analysis reports.
PHC 6050: Statistical Method for the Health Sciences 1
Statistical methods for description and analysis provide investigators with useful tools for making sense of data. The pervasiveness of statistics in public health as well as other fields has led to increased recognition that statistical literacy –familiarity with the goals and methods of statistics should be a basic component of a well-rounded educational program. In this course, students will develop statistical vocabulary, learn methods for descriptive data analysis, study the fundamentals of probability and sampling distributions, learn methods for statistical inference and hypothesis testing based on one or two samples, and become familiar with categorical data analysis and linear regression. Data analysis will be conducted in SPSS.
PHC 6052: Introduction to Biostatistcal Methods
This 3-credit course is a sophisticated introduction to the concepts and methods of biostatistical data analysis. The topics include descriptive statistics, probability, standard probability distributions, sampling distributions, point and confidence interval estimation, hypothesis testing, power and sample size estimation, one and two-sample parametric and non-parametric methods for analyzing continuous or discrete data, and simple linear regression. The SAS statistical software packagewill be taught in this class for data management and statistical analyses.
PHC 6053: Regression Methods for Health and Life Sciences
This course introduces graduate students in fields other than statistics to a wide range of modern regression methods. Emphasis is on modeling driven by actual data from studies in a variety of areas, primarily from health, biology, and ecology. The primary topics are multiple linear regression, logistic regression, and Poisson regression. A main goal is to learn what approach to use among the linear and nonlinear models, and how to determine whether the fit is adequate. By the end of the course, students will achieve competency in carrying out the analyses in SAS.
PHC 6937: Survey of Biostat Methods
TThis course is a survey of biostatistical methods beyond one and two sample techniques covered in PHC 6052. Advanced topics will be selected from areas such as multiple linear regression, study design and ANOVA, contingency tables, logistic regression, Poisson regression, repeated measures and longitudinal data analysis, missing data methods, model/variable selection, survival analysis, multivariate methods, or non-parametric methods. Focus will be on the application of these techniques to data from the health sciences. Examples will make use of SAS and R for this course
STA 6177: Applied Survival Analysis
This course covers survival analysis, Kaplan-Meier estimates, log-rank test, and proportional hazards model.
PHC 6063: Biostatistical Consulting
This course covers communication, management, organization, computational and biostatistical thinking skills necessary to consulting in biostatistics.
PHC 6937: Frontiers in Biostatistics
This course will introduce biostatistics Masters and PhD students to current issues and methods in modern biostatistics research. Current faculty will present selected topics from their current research.