Prerequisites: EPID 600 (or equivalent), EPID 640 (or equivalent), and BIOSTAT 503 or 553 (or equivalent)
Description: This course introduces 1) various sampling methods (simple random sampling, stratified sampling, cluster sampling, convenience sampling, control sampling strategies in case-control design) and 2) power and sample size calculations. This course consists of lectures and hands-on exercises in computer labs, homework assignments, and a final project.
Course Goals: The goal of this course is to learn about how to design surveys with appropriate sampling methods widely used in epidemiologic research and how to compute sample sizes and/or powers given different epidemiologic study designs.
Competencies: After completing this class, students are expected to be able to attain
the following competencies: Be familiar with basic aspects of field methods in epidemiology (e.g. human subject protection, data collection and management, survey design, sampling strategies, calculating power, and public health surveillance).
Specifically, students will be able to:
o Choose and design appropriate sampling methods for different epidemiologic study designs.
o Compute sample size and/or power for different epidemiologic study designs.
Prerequisites: EPID 503 or EPID 600 AND BIOSTAT 503 or BIOSTAT 553
Description: This course will introduce the R statistical programming language for epidemiologic data analysis. This course will focus on core basics of organizing, managing, and manipulating data; basic graphics in R; and descriptive methods and regression models widely used in epidemiology.
Course Goals: The overall goal of the course is to provide students with a set of new data analysis tools for Epidemiology using R.
Competencies: After completing this class, students are expected t-be able t-attain the following Epidemiology Department MPH competencies:
-Be familiar with basic aspects of field methods in epidemiology (e.g. human subject protection, data collection and management, survey design, sampling strategies, calculating power, and public health surveillance) [Epid competency 8].
Specifically, students will be able to
Enter, manage, and manipulate data in R
-Conduct basic data analysis in R
-Graphically display quantitative data in R
EPID675: Data Analysis for Environmental Epidemiology
Description: This course will introduce non-parametric smoothing methods, such as splines, locally weighted polynomial regression (LOESS) and generalized additive models (GAM), and focus on continuous environmental exposure variables. It will also deal with analysis of multi-level data including analyses of longitudinal data and complex sampling data, and time-series analysis that are widely used in environmental epidemiology. The course will cover how to handle limits of detection in environmental exposure data. It will provide an opportunity to analyze actual population data to learn how to model environmental epidemiologic data, and is designed particularly for students who pursue environmental epidemiologic research. The course will consist of lectures and hands-on practices in computer labs, homework assignments and final projects. R, a free software environment for statistical computing and graphics, will be used.
This course is cross-listed with EHS675 in the Environmental Health Sciences department.
Prerequisites: EPID 600, BIOSTAT 523 and BIOSTAT 560 for epid students. Biostat 650, 651 for biostat students
Description: The goal of this pilot course is to create an interdisciplinary educational experience for Ph.D. students in Epidemiology (also available as an optional elective for Masters students in Biostatistics) through a uniquely designed course that contains lectures on advanced biostatistical methods, but places them in the context of epidemiological applications.
Course Goals: Students enrolled in the class will learn about cutting edge statistical methods in these four contemporary topics that arise frequently in the present scientific context. These four topics are: (a) Modern techniques for model building and variable selection; (b) Methods for analyzing longitudinal data; (c) Spatial regression methods; (d) Methods for studies of interaction/effect modification. The course will equip the new generation epidemiologists with state-of-the-art statistical methods in these domains, and teach them the craft of translating a practical problem into mathematical equations. However, the entire theoretical learning process will be placed in the context of sophisticated modeling of data from large complex studies with a focused problem to solve. Data for the projects will come from two studies that Professors Park and Mendes de Leon are involved with: the Normative Aging Study (NAS) and the Chicago Health and Aging Project (CHAP).
Competencies: After completing this class, students are expected to be able to attain the following competencies:
-Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.
-Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.
-Apply descriptive techniques commonly used to summarize public health data.
-Apply common statistical methods for inference.
-Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.
-Interpret results of statistical analyses found in public health studies.
-Develop knowledge to communicate and collaborate effectively with scientists in a variety of health-related disciplines to which biostatistics are applied (e.g. public health, medicine, genetics, biology; psychology; economics; management and policy).
-Become well-versed in the application of core statistical techniques (biostatistical inference, linear regression, generalized linear models, nonparametric statistical methods, linear mixed models) and 4-5 selected statistical specialization techniques.
-Select appropriate techniques and apply them to the processing of data from health studies.
-Interpret the results of statistical analysis and convert them into a language understandable to the broad statistical community.
-Develop written and oral presentation skills and other scientific reporting skills, based on statistical analyses for public health, medical and basic scientists and educated lay audiences
-Employ state-of-the-art statistical and other quantitative methods in the analysis of epidemiologic data.
-Demonstrate a thorough understanding of causal inference, sources of bias, and methods to improve the validity of epidemiologic studies.
-Understand the principles and methods of data-collection and data-processing procedures in the design and conduct of epidemiologic research, with sound knowledge of measurement validity and reliability, data quality control, data management, documentation, and security