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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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.

Prerequisites:

  1. BSTA 511/611 Estimation and Hypothesis Testing for Applied Biostatistics
  2. BSTA 612/613 Linear Models
  3. BSTA 550 Introduction to Probability

BIOST Thesis – BSTA 503

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Advance permission from the Program Director.

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.

Prerequisites:

  1. Students should have adequate skills in at least one statistical program among STATA, SAS or R
  2. BSTA 511/611 Estimation & Hypothesis Testing for Applied Biostatistics
  3. BSTA 512/612 Linear Models

C

Categorical Data Analysis – BSTA 513

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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.

Prerequisites:

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

**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.

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: Statistical Principles of Research Design and Analysis – 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 – PHPM 512/612

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Epidemiology I introduces the concepts, principles and methods of epidemiology. Epidemiology is one of the fundamental sciences used by public health professionals to identify, prevent and control health problems in communities. Specifically, epidemiologic methods are used to investigate the distribution of health- related states or events (e.g. disease, unhealthy exposures, etc.) in populations and identify the factors or characteristics (“determinants”) that influence or determine these distributions. In addition, epidemiology is used to inform and evaluate public health programs and policies and to assist in the development of prevention and control measures to address health problems within communities. In this course, students will learn how to apply epidemiologic methods to answer questions about the distribution of disease, death, disability and risk exposures in populations, as well as those relating to causal relationships between exposures and health outcomes.

Prerequisites: Matriculation into a joint School of Public Health program.

**Note: The following section is offered online: Marshall.
The following sections are offered in-person: Stull, Dinno.
This course is cross-listed as PHPM512/CPH 541/PHE 530.

Estimation and Hypothesis Testing for Applied Biostatistics – BSTA 511

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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.

Prerequisites:

  1. Completion of one undergraduate statistics course.
  2. Current graduate standing or instructor approval.

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 PHPM 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-PHPM 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.

I

Introduction to Probability – BSTA 550

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This course is designed to introduce history, concepts and distributions in probability, Monte Carlo simulation techniques, and Markov chains. Student will also learn how to write R codes for various statistical computations and plots. Previous experience in R is not required.

Prerequisites: One year of calculus (2 semesters or 3 quarters).

L

Linear Models – BSTA 512

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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.

Prerequisite: BSTA 511/611 Estimation & Hypothesis Testing for Applied Biostatistics

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:

  1. BSTA 511/611 Estimation and Hypothesis Testing for Applied Biostatistics
  2. BSTA 612/612 Linear Models
  3. BSTA 513/613 Categorical Data Analysis

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:

  1. BSTA 550 Introduction to Probability
  2. BSTA 551 Mathematical Statistics I
  3. Differential and integral calculus

R

Reading & Research Biostat – BSTA 500

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The student and the instructor plan the course of study consistent with the student’s interest and degree objectives.

Prerequisite: Advance permission from the Program Director.

S

Statistical Machine Learning & Big Data – BSTA 522

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This course is designed to introduce statistical methods for machine learning and new emerging challenges in big data analysis. In recent years, statistical machine learning has played a crucial role in informatics and data science. Ever increasing data size creates new challenges for traditional statistical learning and this is an active research area. This course will cover traditional statistical learning methods as well as newer methods for such challenges.

Prerequisites:

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

Statistical Methods for Next Generation Sequencing Data – BSTA 524

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This course is designed to introduce the statistical theory and methods for next generation sequencing data (NGS). In recent years, NGS has been the choice of platform for genomic studies. Due to the high dimensionality of NGS, it provides unique challenges in statistical analysis and requires different statistical methods. Although NGS data are the main focus, the theory and methods are applicable to other high dimensional data such as microarray and proteomics. This course will cover statistical theory and methods specialized for NGS and other high dimensional data. It is strongly recommended that students bring their own laptop computers to classes given.

Prerequisites: BSTA 512/612 or consent of instructor.

Statistical Methods in Clinical Trials – BSTA 517

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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.

Prerequisite: BSTA 511/611 Estimation & Hypothesis Testing for Applied Biostatistics

**Note: This course is online.

Survival Analysis – BSTA 514

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Survival analysis is the modern name given to the collection of statistical procedures which accommodate time-to-event data, having as a principal endpoint the time when an event occurs. Such events are generally referred to as failures. Some examples are time to failure of an electrical component, time to first recurrence of a tumor (i.e., length of remission) after initial treatment, time to death, time to the learning of a skill, etc. In these examples it is possible that a failure time will not be observed either by deliberate design or due to random censoring. This occurs, for example, if a patient is still alive at the end of a clinical trial period or has moved away. The necessity of obtaining methods of analysis that accommodate censoring is the primary reason for developing specialized models and procedures for failure time data.

Prerequisites: One year of calculus (two semesters or three terms).

**Note: This course is offered at alternating campuses each year, and is cross-listed as STAT 578. OHSU students who wish to take this course at PSU must submit an intercampus registration form.

Meet our Faculty

David Bangsberg

Founding Dean

Dr. Ryan Petteway – A People’s Social Epidemiologist

Assistant Professor
Office: PSU – URBN 470N