Description: This course will introduce the theory and methods of spatial and spatio-temporal statistics. It will present spatial and spatio-temporal statistical models and will discuss methods for inference on spatial processes within a geostatistical and a hierarchical Bayesian framework.
Advisory Prerequisites: Previous expericne with R is preferred, not required
Description: With the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data, covering modeling approaches for the two different types of spatial data: point-referenced data, where the geographical coordinates of the observations have been recorded; and areal-averaged data, where summary statistics (e.g., number of disease cases by county, zip code, etc.) are reported for each areal unit. Topics covered include: exploratory analysis for spatial data, covariance functions, kriging, spatial regression; disease mapping, spatial smoothing; point processes, assessment of clustering, and cluster detection. Each lecture will feature a lab component, during which spatial analyses of datasets, made available to the participants, will be performed using the publically available R statistical software
Course Goals: With the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data
Competencies: With the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data
EPID815: Modern Statistical Methods in Epidemiologic Studies
Prerequisites: EPID 600, BIOSTAT 523 and BIOSTAT 560 for epid students. Biostat 650, 651 for biostat students
Advisory Prerequisites: EPID 798 for epid students; BIOSTAT 695 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