Course Schedules
Download the OHSU-PSU School of Public Health’s 2023 – 2024 academic year course schedule.
Last Updated: 10.9.2023
Course Schedules by Term
Summer 2023 Planning Schedule
Last updated 5.2.2023
Download Summer Planning Schedule
Fall 2023 Planning Schedule
Last updated 5.18.2023
Download Fall Planning Schedule
Winter 2024 Planning Schedule
Last updated 10.20.2023
Download Winter Planning Schedule
Spring 2024 Planning Schedule
Last updated 10.9.2023
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.
Applied Longitudinal Data Analysis – BSTA 519
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 519 | 3 |
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
Bayesian Methods for Data Analysis – BSTA 521
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 521 | 3 |
Course Information
The methods students learned in the biostatistical applied and theoretical sequences were based on the “frequentist” method of statistical reasoning, where probability is understood to be the long-run frequency of a ‘repeatable’ event, and statistics that are computed are based on a specific study only. Bayesian methods are based on a different philosophy – that probability of an event is based on ALL information known at the time. Bayesian methods for data analysis enable one to combine information from previous similar and independent studies (prior information), with information from a new study, yielding updated inference for model parameters. This course will cover the concept of Bayesian analysis, posterior distribution, Bayesian inference and prediction, prior determination, one parameter and two parameter models, Bayesian hierarchical models, Bayesian computation, model criticism and selection as well as basic comparison of Bayesian and Frequentist Inferences. Real life examples in medical and health science will be used to explain the concept and application of Bayesian models.
Prerequisites
BSTA 511/611 Estimation and Hypothesis Testing for Applied Biostatistics (passing grade of "B" or better); BSTA 512/612 Linear Models; and BSTA 550 Introduction to Probability (passing grade of "B" or better).
Biostatistics Lab – BSTA 530
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 510 | 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 has finished BSTA 512 linear Models or equivalent. Students meet weekly for 1 hour with the course instructor for discussion on their projects and are also encouraged to have regular meetings with an assigned faculty advisor and/or
consultee(s). Students are expected to work individually or in a team of 2~3 on actual data analysis. The workload will be at least 9 hours per week including all activities (classes, meetings, readings, coding, and analysis).
Prerequisites:
- BSTA 511/611 Estimation & Hypothesis Testing for Applied Biostatistics
- BSTA 512/612 Linear Models
Categorical Data Analysis – BSTA 513 / 613
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 513/613 | 4 |
Course Information
Categorical Data Analysis (Biostatistics III) is the third course in the required sequence for Biostatistics Certificate Program and the MPH EPI and MPH Biostats programs.
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, topics for logistic regression will include parameter interpretation, statistical adjustment, variable selection techniques and model fit assessment. Students will have the opportunity to briefly explore other analysis methods, such as Poisson regression, ordinal logistic regression, etc. Most homework assignments for this course are to be completed using statistical software.
Doctoral students register for the BSTA 613 section.
Prerequisites:
- BSTA 511/611 Estimation & Hypothesis Testing for Applied Biostatistics
- BSTA 512/612 Linear Models
Concepts of Environmental Health – ESHH 511 / 611
| Course Code | Credit | |||
|---|---|---|---|---|
ESHH 511 / 611 | 3 |
Course Information
An intensive course designed to familiarize students with fundamentals of environmental health from a scientific and conceptual perspective. Topics are considered within multi-causal, ecological, adaptive systems, and risk-assessment frameworks. Includes consideration of biological, chemical, and physical agents in the environment, which influence public health and well-being.
Doctoral students register for the ESHH 611 section.
Data Management & Analysis in SAS – BSTA 515
| Course Code | Credit | |||
|---|---|---|---|---|
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. After brief introduction, the course will cover intermediate to early advanced level programming skills in 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 use. This course could be extremely helpful in preparation for thesis, capstone or other research projects.
Prerequisites:
- BSTA 511/611 Estimation & Hypothesis Testing for Applied Biostatistics
- BSTA 512/612 Linear Models
Design and Analysis of Surveys – BSTA 516
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 516 | 3 |
Course Information
This course is designed to introduce basic concepts, techniques, and current practice of sample survey design and analysis with emphasis on community health surveys. Specific topics covered include introduction to instrument design and evaluation, and statistical sample design (including simple random sampling, systematic sampling, stratified random sampling, cluster sampling, multistage sampling, and replicated sampling). Examples of complex designs will be drawn from telephone surveys, the Current Population Survey and various health surveys of National Center for Health Statistics (NCHS). Topics in estimation and analysis include probability weighting, weight adjustments based on auxiliary data, ratio and regression estimators, and methods for estimating variance from complex surveys. Analysis of complex data will be illustrated using STATA 13 and R and taking examples from complex surveys of NCHS.
Prerequisite: BSTA 511/611 Estimation & Hypothesis Testing for Applied Biostatistics
Design of Experiments – BSTA 523
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 523 | 3 |
Course Information
This course covers an experimental design and statistical analysis of biological/clinical data from various experiments. This course provides not only theoretical aspect of experimental design but also hand-on experience in designing and analyzing experiments. The course begins design principles that include concepts of replication, randomization, blocking, multifactor studies, and confounding. Basic matrix algebra concepts will be explored to establish the basis for linear models. Students, then, are introduced to various experimental designs including analysis of variance (ANOVA) in both single and multi-factorial setting, experiments to study variances, complete/incomplete block designs (CBD), split plot design, repeated measures ANOVA, analysis of covariance (ANOCOVA), response surface design, and diagnosing agreement between the data and model. The course also provides experience in analyzing unbalanced experimental. Computer application is included as part of the course to introduce students to data management, reading output, interpreting and summarizing results.
Prerequisites: BSTA 511/611 Estimation & Hypothesis Testing for Applied Biostatistics or equivalent.
Epidemiology I – EPI 512 / 612
| Course Code | Credit | |||
|---|---|---|---|---|
EPI 512 / 612 | 4 |
Course Information
This is the first course in a three course sequence designed for MPH Epidemiology and Biostatistics majors. Textbook based; e.g. Gordis Epidemiology. Basic epidemiological principles applicable to infectious and non-infectious diseases, host-agent-environmental relationships, and concepts of disease causation will be reviewed. Students will gain familiarity with epidemiologic measures such as incidence, prevalence, mortality, natality, case fatality, relative risk and other rates and ratios and will use age-adjustment and other standardization techniques. Types and sources of public health data will be reviewed, their use in comparing groups, and statistical significance. Epidemic curves, outbreak investigation principles, surveillance concepts and basic designs of observational studies and sources of bias will be covered.
Students in the MPH Epidemiology and MPH Biostatistics programs should take the on-campus Epi I course.
Doctoral students register for the EPI 612 section.
Estimation and Hypothesis Testing for Applied Biostatistics – BSTA 511 / 611
| Course Code | Credit | |||
|---|---|---|---|---|
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 packages, interpreting and summarizing results.
Doctoral students register for the BSTA 611 section.
Foundations of Public Health – PHE 511
| Course Code | Credit | |||
|---|---|---|---|---|
PHE 511 | 3 |
Course Information
Provides students with an understanding of the field of public health. It provides knowledge about public health principles, concepts, values, tools, and applications. Key topics in the class include the mission of public health, the politics of public health, determinants of health in the United States, major models and strategies for health promotion, and community perspectives on public health interventions.
Health Systems Organization – HSMP 574 / 674
| Course Code | Credit | |||
|---|---|---|---|---|
HSMP 574 / 674 | 3 |
Course Information
This course introduces basic concepts and issues in the organization, financing, and delivery of health services. The emphasis is on the systemic aspects of health services production and delivery which address the health needs of populations with respect to death, disease, disability, discomfort, and dissatisfaction. Students will examine the inter-relationships of system structures, subsystems, and processes, as well as their interactions with the larger social, cultural, economic and political environments in which they exist. The focus is on the United States, with international comparisons used to illustrate similarities and differences.
Introduction to Probability – BSTA 550
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 550 | 3 |
Course Information
This course is designed to introduce histroy, concepts and distributions in probability, Monte Carlo simulation techniques, and Markov chains. Students will also learn how to write R codes for various statistical computations and plots. Previous experience in R is not required. R is free software available from http://www.r-project.org.
Prerequisites: One year of calculus (2 semesters or 3 quarters).
Introduction to Public Health – PHE 513
| Course Code | Credit | |||
|---|---|---|---|---|
3 |
Research
This survey course intended to provide graduate students with foundational knowledge of public health and will take a population science approach to public health practice.
Linear Models – BSTA 512 / 612
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 512/612 | 4 |
Course Information
This course is the second course in the required sequence for all Graduate Biostatistics program, the MPH Epidemiology track, and the PhD Epidemiology program. This course expands on the analyses techniques presented in BSTA 511. In particular, we focus on multiple regression analysis and various analysis of variance techniques ending with a conceptual overview of techniques for correlated continuous outcomes (i.e., random effects and repeated measures). Classes consist of lecture, examples of data analysis and Stata and/or R computer application techniques. Written homework assignments and data analysis projects are used to assist in mastery of the analysis methods.
Doctoral students register for the BSTA 612 section.
Prerequisites
BSTA 511 / 611, "Estimation & Hypothesis Testing for Applied Biostatistics."
Practice Experience – BSTA 509PE / CPH 509PE / EPI 504PE / ESHH 509PE / HSMP 509PE / PHE 504PE
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 509PE / CPH 509PE / EPI 504PE / ESHH 509PE / HSMP 509PE / PHE 504PE | 4 |
Course Information
Consent of PE Coordinator required.
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.
Prerequisites
Consent of PE Coordinator required
Qualitative Methods for Health Professionals – UNI 504
| Course Code | Credit | |||
|---|---|---|---|---|
UNI 504 | 2 |
Course Information
This course is designed for students from across health and science disciplines to obtain hands-on experience in qualitative research methods. The 2 credit course is designed to promote collaboration across disciplines through an introduction to qualitative approaches, such as interviews, focus groups, and observational procedures, which can be applied across research disciplines as a sole methodology or as part of a mixed-methods design. Students will work in interprofessional teams to plan for and engage in basic data collection and analysis, with a focus on study design, sampling and selection, budgeting for qualitative tasks, data management, coding, content analysis and reporting. Attention will be paid to the specific issues of ethics and confidentiality in qualitative research, as well as the unique challenges of rigor and reproducibility as they apply to qualitative methods. At the end of the course, students will be able to select an appropriate qualitative method, implement it with their target population, analyze the results, and present it clearly.
Statistical Analysis of Time-to-Event Data – BSTA 514
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 514 | 3 |
Course Information
This course introduces students to analysis of survival (i.e. time-to-event) 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.
Prerequisites:
A standard pre-calculus course in probability & statistics (e.g. BSTA 511)
A course in applied linear regression models (e.g. BSTA 512)
Calculus is preferred
Statistical Inference I – BSTA 551
| Course Code | Credit | |||
|---|---|---|---|---|
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.
Prerequisites:
- BSTA 550 Introduction to Probability
- Differential and integral calculus
Statistical Inference II – BSTA 552
| Course Code | Credit | |||
|---|---|---|---|---|
BSTA 552 | 3 |
Course Information
The objectives of the two term sequence are to (1) provide students with fundamental principles for conducting statistical inference both via estimation and hypothesis testing and (2) develop the mathematical skills for applying these principles in new situations. In the first term we focus on principles of data reduction and estimation, but will also introduce hypothesis testing if time permits. In the second term we focus on hypothesis testing, interval estimation, and asymptotic results.
Prerequisites:
- BSTA 551 Statistical Inference I
- BSTA 550 Introduction to Probability
- Differential and integral calculus
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