Courses Taught by Kevin (Zhi) He
BIOSTAT653: Theory and Application of Longitudinal Analysis.
- Graduate level
- Residential
- Fall term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Zhenke Wu, Kevin (Zhi) He, (Residential);
- Prerequisites: BIOSTAT650 and concurrent enrollment in BIOSTAT651
- Description: This course overviews the statistical models and methodologies for analyzing repeated measures/longitudinal data. It covers general linear models and linear mixed models for analyzing correlated continuous data, as well as marginal (i.e. GEE), conditional (i.e. generalized linear mixed model) and transition models for analyzing correlated discrete data.
- Syllabus for BIOSTAT653
| Department | Program | Degree | Competency | Specific course(s) that allow assessment | BIOSTAT | MS | Perform ANOVA analysis and longitudinal data analysis | BIOSTAT653 | BIOSTAT | PhD | Perform statistical analysis for longitudinal data and correlated data | BIOSTAT653 |
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EPID742: Generalized Linear Models
- Graduate level
- Residential
- Summer term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): Kevin (Zhi) He (Residential);
- Prerequisites: None
- Advisory Prerequisites: Introductory level courses in epidemiology and biostatistics.
- Description: Course will cover regression methods for continuous, binary, and count data. The majority of epidemiologic data involve either binary or count data, and binary data often arise from underlying continuous data. Therefore, linear , logistic ,and Poisson regression analyses are important analytic approaches that provide valuable insights into data collected.
EPID784: Survival Analysis Applied To Epidemiologic And Medical Data
- Graduate level
- Residential
- Summer term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): Douglas Schaubel, Kevin (Zhi) He, (Residential);
- Offered Every Summer
- Prerequisites: An introductory course in (bio)statistics that covers regression is required. Familiarity with a statistical software package is helpful, but example code will be provided in labs.
- Advisory Prerequisites: The mathematical level is completely accessible with knowledge of high school algebra, one semester of calculus, and a one-year course in basic statistical methods.
- Description: The primary objectives of this course are to provide participants with the background required to understand commonly used survival analysis methods and to apply such methods using standard statistical software. The course material relies heavily on examples and intuitive explanations of concepts.
