Oregon Health Sciences University (OHSU) and Portland State University (PSU) School of Public Health

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Course Directory and Schedules

Course Schedules

Course Schedules by Term

Fall 
Fall 2022 GR Courses
Planning Schedule

Winter
Winter 2023 GR Courses
Planning Schedule

(rev. 7.5.22)

 

Spring
Spring 2023 GR Courses
Planning Schedule
(rev. 7.5.22)

Interprofessional Education Course Schedule

Interprofessional education occurs when students from two or more professions learn about, from, and with each other to enhance collaboration and improve health outcomes. At least 1 credit of interprofessional education is required by all MPH degree programs.

Most courses with OHSU subject code IPE (Inter-Professional Education) or UNI (University Curriculum) satisfy the interprofessional education requirement. Other courses may also serve; consult your advisor. For a list of IPE and UNI courses, descriptions, and their intended schedule, download this spreadsheet.This list is subject to change — contact the course instructor if you would like to enroll.

SPH Course Descriptions

Descriptions of all School of Public Health courses can also be found in the course catalog of the most recent edition of the PSU Bulletin.

  • The results are being filtered by the organization: Applied Longitudinal Data Analysis - BSTA 519

Applied Longitudinal Data Analysis – BSTA 519

Course Information

This course is designed for students who have taken the basic applied statistical courses and wish to learn the more advanced statistical methods for longitudinal data. Longitudinal data consist of measurements of response variables at two or more points in time for many individuals. This course covers the statistical properties of longitudinal data and special challenges due to the repeated measurements on each individual, exploratory methods and statistical models for longitudinal data as well as some exposure to estimation methods and statistical properties of estimates. For statistical methods, the course will briefly cover the more traditional repeated measure analysis of variance (ANOVA) approach for continuous data, and focus more on mixed effects model approach and estimation based on generalized estimating equation. Real life examples will be used to explain the concept and application of these models by using continuous, binary and count data. Homework assignments and final class project play a central role to understand and appropriately apply the methods covered in the course.

Prerequisites:

  • BSTA 511/611 Estimation and Hypothesis Testing for Applied Biostatistics
  • BSTA 512/612 Linear Models
  • BSTA 513/613 Categorical Data Analysis
Course Code

BSTA 519

Credit

3