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Graduate Course Directory and Schedules

Course Schedules

Download the OHSU-PSU School of Public Health’s 2024 – 2025 academic year course schedule.

Last Updated: 5.10.2024

Download Planning Schedule

Course Schedules by Term

Summer 2024 Planning Schedule

Last updated 4.19.2024
Download Summer Planning Schedule

Fall 2024 Planning Schedule

Last updated 5.10.2024
Download Fall Planning Schedule

Winter 2025 Planning Schedule

Last updated 5.10.2024
Download Winter Planning Schedule

Spring 2025 Planning Schedule

Last updated 5.10.2024
Download Spring Planning Schedule

View previous academic years course schedules per term for School of Public Health students – Archived GR Schedules.

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.

BSTA 502IP – Integrative Project

Course CodeCredit

BSTA 502IP

Course Information

The key culminating step for each MPH student is the Integrative Project (IP). Through the IP, a high-quality written product is produced, which we call the “IP paper.” Through the IP paper, students demonstrate their academic learning and public health practice skills through the synthesis of foundational and program competencies and application of those competencies to complex public health issues. The paper will take the form of a substantial written product such as a program evaluation, policy or economic analysis, grant proposal, health promotion or community engagement program plan, publishable manuscript, or other written product that demonstrates integration of three foundational (one must include Foundational Competency #6) and three program competencies. Appropriate types of written products vary by program, type of practice experience (if the two are integrated), and the student’s career goals. We recommend (but do not require) that the IP paper build upon work conducted in the Practice Experience. For example, students may write a high-quality written paper using the findings from a statistical analysis performed in support of a research project that is separate from their Practice Experience.

More Information About the Integrative Project

BSTA 504 – Data Equity for Health Professionals

Course CodeCredit

BSTA 504

BSTA 509PE – Practice Experience

Course CodeCredit

BSTA 509PE

4

Course Information

Students must attend a PE orientation (via canvas.pdx.edu) prior to registering and are encouraged to attend a PE info session. Detailed information about the PE can be found on the Practice Experience SPH webpage.

PEs are a total of 4 credits and 160 “contact hours.” Students demonstrate 5 competencies via at least two deliverables, as well as submit a learning agreement (the term before PE registration), a midway progress report, a portfolio, and perform an oral presentation.

Biostats students register for BSTA 509PE. PHP students register for CPH 509PE. Epi students for EPI 504PE. ESHH students register for ESHH 509PE. HSMP students register for HSMP 509PE. HP students register for PHE 504PE.

Prerequisite

Consent of PE Coordinator required.

BSTA 511 / 611 – Estimation and Hypothesis Testing for Applied Biostatistics

Course CodeCredit

BSTA 511/611

4

Course Information

This course covers a broad range of basic statistical methods used in the health sciences. The course begins by covering methods of summarizing data through graphical displays and numerical measures. Basic probability concepts will be explored to establish the basis for statistical inference. Confidence intervals and hypothesis testing will be studied with emphasis on applying these methods to relevant situations. Both normal theory and nonparametric approaches will be studied including one- and two-sample tests of population means and tests of independence for two-way tables. Students will be introduced to one-way analysis of variance (ANOVA), correlation, and simple linear regression. The course focuses on understanding when to use basic statistical methods, how to compute test statistics and how to interpret and communicate the results. Computer applications are included as part of the course to introduce students to basic data management, reading output from computer pack-ages, interpreting and summarizing results.

 

Slash Listed Courses

Doctoral students register for the BSTA 611 section.

BSTA 512 / 612 – Linear Models

Course CodeCredit

BSTA 512/612

4

Course Information

BSTA 512 is primarily designed for Biostatistics Graduate Certificate students in Department of Public Health and Preventive Medicine, and BSTA 612 for PhD students from Behavioral Neuroscience or other PhD programs. In this course, we will focus on Linear models that include Regressions Analysis and Analysis of Varience (ANOVA). In conjunction with the conceptual and theoretical supporting the topics. For students of BSTA 612, extra homework problems and reading materials will be assigned along with one extra week of lecture on mixed-effects models for longitudinal/repeared measure data.

 

Slash Listed Courses

Doctoral students register for the BSTA 612 section.

BSTA 513 / 613 – Categorical Data Analysis

Course CodeCredit

BSTA 513/613

4

Course Information

Categorical Data Analysis is the third course in the required sequence for applied Biostatistics (Bsta 511, Bsta 512, Bsta 513 or Bsta 611, Bsta 612, Bsta 613). This course covers topics in categorical data analysis such as cross tabulation statistics, statistics for matched samples, and methods to assess confounding and interaction via stratified tables. Students will learn logistic regression, and relate results back to those found with stratified analyses. Similar to linear regression in Bsta 512/Bsta 612, topics for logistic regression will include parameter interpretation, statistical adjustment, variable selection techniques and model fit assessment. Students will have the opportunity to be exposed to other analysis methods, such as Poisson regression and multinomial logistic regression, etc. We will also learn some machine learning techniques other than logistic regression model. Most homework assignments for this course require the use of statistical software.
 

Prerequisites:

  1. BSTA 511/611: Estimation & Hypothesis Testing for Applied Biostatistics
  2. BSTA 512/612: Linear Models

Slash Listed Courses

Also offered as BSTA 613 for doctoral students.

BSTA 514 – Statistical Analysis of Time-to-Event Data

Course CodeCredit

BSTA 514

3

Course Information

This course introduces students to analysis of time-to-event (i.e. survival) data, covering methods for estimation, hypothesis testing, and regression methods for censored data with covariates. Methods widely used in the health sciences are covered, including Kaplan-Meier (empirical) estimate of the survival function and its associated statistical tests. The Cox proportional hazards regression model is presented in detail, along with some extensions of this model. As time allows, other topics will be introduced including parametric survival models, frailty models and/or models incorporating competing risks. Power and sample size computations for time-to-event data will also be introduced. Most assignments will be completed using statistical computing software. Contextualizing results in the context of health sciences problems and research questions is stressed throughout the course.

Prerequisite

A standard pre-calculus course in probability & statistics (e.g. BSTA 511), a course in applied linear regression models (e.g. BSTA 512).

BSTA 515 – Data Management & Analysis in SAS

Course CodeCredit

BSTA 515

3

Course Information

This course is designed for students who want to develop and expand their skills in data management, statistical analyses and graphics for the real world applications using SAS. The course will give students opportunities to build their data management programing skills from basic to advanced levels in SAS. As part of the course competencies, students will have chance to learn how to export SAS data sets and create ODS files for Microsoft Excel. Students will have chance to build their analysis skills from basic to advanced levels using SAS. The class will be taught in a computer lab in order to give the student hand on experience using SAS to manage data, perform analyses and produce graphs. Class sessions and homework will be oriented around particular data management and analysis tasks. Health-related data sets will be provided for students to practice. This course could be extremely helpful in preparation for thesis, capstone or other research projects.

BSTA 516 – Design and Analysis of Surveys

Course CodeCredit

BSTA 516

3

Course Information

This course is designed to introduce basic concepts, techniques, and current practice of sample survey design and analysis. Specific topics covered include introduction to statistical sample design, such as simple random sampling, systematic sampling, stratified random sampling, cluster sampling, multistage sampling. Complex designs will also be included. Topics in estimation and analysis include probability weighting, weight adjustments, ratio and regression estimators, and methods for estimating variance from complex surveys. In conjunction with the conceptual and theoretical developments, homework assignments and data analysis projects will be assigned in supporting the topics.

BSTA 517 – Statistical Methods in Clinical Trials

Course CodeCredit

BSTA 517

3

Course Information

This is an online course designed for students and researchers who are interested in learning statistical methods in the design and analysis of clinical trials. Students are expected to have certain statistical background in order to gain deep understanding to the topics covered in this course. Topics to be discussed in the course include introduction to clinical trials, fundamentals of Bayesian statistics, sample size computation for trials with dichotomous, continuous and time-to-event outcomes, methods of randomization, design challenges for oncology clinical trials, Bayesian methods in clinical trials, adaptive clinical trial design and designs for predictive biomarkers.

BSTA 519 – Applied Longitudinal Data Analysis

Course CodeCredit

BSTA 519

3

Biostatistics
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 coefficient estimates. For statistical methods, the course will briefly mention the 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

BSTA 525 – Introduction to Biostatistics

Course CodeCredit

BSTA 525

Course Information

The goal of this course is to cover the broad range of statistical methods used in health sciences. Methods of summarizing data through graphical displays and numerical measures will be discussed. Basic probability concepts will be explored to establish the basis for statistical inference. Confidence intervals and hypothesis testing will be studied with emphasis in applying these methods to relevant situations. Both normal theory and non-parametric approaches will be studied. Course focus will be to understand when to use basic statistical methods how to compute tests to statistics and how to interpret results. Computer applications (using statistical software) are included as part of the course.

Prerequisite

Graduate standing

BSTA 526 – R programming for Health Data Science

Course CodeCredit

BSTA 526

Course Information

This course aims to develop programming skills in R, a powerful statistical programming language. This course assumes some prior familiarity with R and ranges from advanced beginner topics to intermediate topics. It will cover practical data science skills in R that are useful for a career in statistics, epidemiology, or data science, including loading data, data wrangling, visualization, automation, machine learning, and running statistical models. A laptop is required for class to participate in coding exercises.

Prerequisite

BSTA 511 or instructor approval.

BSTA 530 – Biostatistics Lab

Course CodeCredit

BSTA 530

3

Course Information

The course provides hands-on data analysis and/or biostatistical consulting experience to students outside classroom settings. Students will have opportunities to perform data analysis with inputs from faculty members. Students should have adequate skills in at least one statistical program among STATA, SAS or R and finished BSTA 512 Linear Models or equivalent. Students meet weekly for 1~2 hour with the course instructor for discussion on their projects and are required to have regular meetings with an assigned faculty advisor and/or consultee(s), if applicable. Students are expected to work individually or in a team of 2~3 on actual data analysis. In addition, there is weekly reading assignment. The workload will be at least 9 hours per week including all activities (classes, meetings, readings, coding, and analysis).
 

Prerequisites:

  • BSTA 512/612: Linear Models

BSTA 550 – Introduction to Probability

Course CodeCredit

BSTA 550

3

Course Information

This course is an introduction to the methods and concepts of probability theory, including combinatorics, conditional probability and independence, discrete and continuous random variables, probability distributions, joint distributions, expectation, transformations of random variables, moment generating functions, and the central limit theorem.
 

Prerequisites

Acceptance to MS in Biostatistics program.

BSTA 551 – Statistical Inference I

Course CodeCredit

BSTA 551

3

Course Information

Statistical Inference I is the first course of a two term course (BSTA 551 & 552) covering the foundations of statistical inference. It is targeted to graduate students majoring in biostatistics and other disciplines requiring an understanding of statistical theory. The course starts with a review of the probability theory that is the basis for that inference. We will then focus on principles of data reduction and estimation (frequentist and Bayesian methods). We will also introduce hypothesis testing, time permitting. The two courses must be taken in sequence.

Prerequisites

  1. BSTA 550 Introduction to Probability
  2. Differential and Integral calculus

BSTA 552 – Statistical Inference II

Course CodeCredit

BSTA 552

3

Course Information

Statistical inference II is the second of a two term course (BSTA 551 & BSTA 552) and provides theoretic foundation in biostatistics. Topics in the second course will include sampling distributions, point and interval estimation, tests of hypotheses, and an introduction to asymptotic theory. The two courses must be taken in sequence.

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 the spreadsheet. This list is subject to change, contact the course instructor if you would like to enroll.

Interprofessional Education