Teaching Fellow for Graduate-Level Biostatistics Courses
Here are descriptions of the three full-semester courses that I helped teach at HSPH.
Spring 2024: BST 226, Applied Longitudinal Analysis
Approx. number of students: 100
Course description: This course covers modern methods for the analysis of repeated measures, correlated outcomes and longitudinal data, including the unbalanced and incomplete data sets characteristic of biomedical research. Topics include an introduction to the analysis of correlated data, analysis of response profiles, fitting parametric curves, covariance pattern models, random effects and growth curve models, and generalized linear models for longitudinal data, including generalized estimating equations (GEE) and generalized linear mixed effects models (GLMMs).
Fall 2023: BST 210, Applied Regression Analysis
Approx. number of students: 75
Course description: Topics include model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course provides students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature.
Fall 2021: BST 222, Basics of Statistical Inference
Approx. number of students: 75
Course description: This course provides a basic yet thorough introduction to the probability theory and mathematical statistics that underlie many of the commonly used techniques in public health research. Topics covered include probability distributions (normal, binomial, Poisson), means, variances and expected values, finite sampling distributions, parameter estimation (method of moments, maximum likelihood), confidence intervals, hypothesis testing (likelihood ratio, Wald and score tests). All theoretical material are motivated with problems from epidemiology, biostatistics, environmental health and other public health areas. This course is aimed towards second year doctoral students in fields other than Biostatistics. Background in algebra and calculus required.
