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

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Our Faculty

More than 150 faculty members work within the OHSU-PSU School of Public Health. They have a wide range of expertise, from monitoring and assessing health risks and opportunities in populations, to helping build health-supporting social environments through policy, advocacy, and programs. They are educators, advisors, researchers, practitioners and community leaders. They come from backgrounds in quantitative, behavioral, environmental and social sciences, policy and government, exercise and health sciences and anthropology, among many other areas. They all work in collaboration with each other and with community partners, and are especially focused on the training and education of future leaders and practitioners in the public health fields.

 

Programs

Home » Courses by Program » Biostatistics
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A

Applied Longitudinal Data Analysis – BSTA 519

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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

Applied Practice Experience – CPH 513

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The Applied Practice Experience course will facilitate development and completion of a portfolio that requires students to demonstrate attainment of selected competencies. Students will study portfolio use and development in higher education.

It is recommended that students have completed at least 18 credits of their MPH program to have garnered enough experiences to be used in the portfolio assignment. Students will not be allowed to register for this course during their first term of their MPH program.

Grade mode: Pass / No Pass.

B

Bayesian Methods for Data Analysis – BSTA 521

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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.

Offered in Spring term in even years only.

Biostatistics Lab – BSTA 510

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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

C

Categorical Data Analysis – BSTA 513 / 613

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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.

**Note: This course is offered at OHSU, and is cross-listed at PSU as STAT 577. PSU students who wish to take this course must submit an intercampus registration form.

Prerequisites:

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

Concepts of Environmental Health – ESHH 511 / 611

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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.

D

Data Management & Analysis in SAS – BSTA 515

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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:

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

Design and Analysis of Surveys – BSTA 516

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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

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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.

E

Epidemiology I – EPI 512 / 612

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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 / Hypothesis Testing for Applied Biostatistics – BSTA 511 / 611

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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.

F

Field Experience – BSTA 507

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The Field Experience provides the opportunity to apply the statistical methods learned in the classroom to important public health problems and to develop the ability to synthesize and integrate knowledge. With the assistance of faculty and Biostatistics Field Experience Coordinator, students will select a field experience that is aligned with their interests and goals, and will be required to have some data analysis and/or study design component. Placements may include, but not limited to, state and county health departments, health policy research institutes, practice networks and public health activities conducted by non-Biostats OHSU investigators. The 6 credits may be taken over two quarters. The Field Experience is part of the culminating experience for the MPH in Biostatistics degree and will require a minimum of 200 hours.

Foundations of Public Health – PHE 511

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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.

G

Global Health Program Evaluation & Mgmt – HSMP 590

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Program evaluation is a field of study and practice that is applicable across areas and disciplines. This course provides students with the theoretical and practical bases for the trans-discipline of program evaluation. The course emphasizes evaluation in the context of global health programs. Students will develop basic skills in a variety of approaches to evaluation, including techniques that are particularly suitable for evaluating global health programs.

H

Health Systems Organization – HSMP 574 / 674

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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.

The following sections are offered online: Baker. All other sections are offered in-person.

I

Integrative Learning Experience – BSTA 506

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The Integrative Learning Experience (ILE) requires MPH students to synthesize selected competencies from the Council on Education for Public Health (CEPH). The ILE involves students working with preceptors at organizations outside of the SPH. During the ILE, students will create a substantial written product that is appropriate for each student’s educational and professional objectives as well as complete other required assignments. The ILE occurs toward the end of a student’s program of study.

Introduction to Probability – BSTA 550

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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).

L

Linear Models – BSTA 512 / 612

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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.

M

Mathematical Statistics I – BSTA 551

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Mathematical Statistics 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:

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

Mathematical Statistics II – BSTA 552

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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 Mathematical Statistics I (passing grade of “B” or better)
  • BSTA 550 Introduction to Probability
  • Differential and integral calculus
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Meet our Faculty

David Bangsberg

Founding Dean

Dr. Ryan Petteway – A People’s Social Epidemiologist

Assistant Professor
Office: PSU – URBN 470N