Health Statistics Concentration

Course #TitleCompetenciesCredits
STAT 512 Applied Regression Analysis (Fall, Spring, Summer) 2,3 3
STAT 514 Design of Experiments (Fall, Spring) 1,2,3 3
STAT 522 Sampling & Survey Techniques (Spring Only) (Pre-Req STAT 512) 2,3,4,5 3
Category A: Concentration Selectives (Choose 3 credits)
ASM 540 Geographic Information Systems Application 2,3, 5 3
CE 597 Geospatial Modeling and Analysis (Pre-Req GIS Introduction) 2,3,5 3
CE 597 Geographic Information Systems (Spring) 2,3,5 3
CPB 619 Design, Conduct and Analysis of Clinical Trials 1,3 2
CPB 625 Clinical Biostatistics (Spring) 1,2 2
CPB 626 Design and Analysis of Epidemiologic Studies (Odd Fall) 1,2,3,5 3
HDFS 629 Family and Couple Interventions in Health Problems (Spring) 1,5 3
SOC 681 Structural Equation Modeling (need own research data) 2,3,5 3
Category B: STAT Electives (Optional)
STAT 506 Statistical Programming and Data Management (Fall, Spring) 2 3
STAT 524 Applied Multivariate Analysis (Fall Only) (Pre-Req STAT 512) 1,2,3 3
STAT 582 Statistical Consulting and Collaboration (Pre-Req STAT 525) 2,3 3

Guidelines: For the health statistics concentration STAT 512, STAT 514 and STAT 522 must be included on the plans of study. Plans of study must also include at least three credits from category A. The remaining three credits can be from category A or B. Additionally, all health statistics competencies must be thoroughly covered. 

STAT 506 — Statistical Programming and Data Management

Use of the SAS software system for managing statistical data. The SAS environment. Data description. Data access and management. SAS macro language and application development. 

STAT 512 — Applied Regression Analysis

Inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. 

STAT 514 — Design of Experiments

Fundamentals, completely randomized design; randomized complete blocks; latin square; multi-classification; factorial; nested factorial; incomplete block and fractional replications for 2n, 3n, 2m x 3n; confounding; lattice designs; general mixed factorials; split plot; analysis of variance in regression models; optimum design. 

STAT 522 — Sampling and Survey Techniques

Survey designs; simple random, stratified and systematic samples; systems of sampling; methods of estimation; costs.

STAT 524 — Applied Multivariate Analysis

Extension of univariate tests in normal populations to the multivariate case, equality of covariance matrices, multivariate analysis of variance, discriminant analysis and misclassification errors, canonical correlation, principal components, factor analysis. Strong emphasis will be placed on use of existing computer programs. 

STAT 549 — An Introduction to QTL Mapping in Experimental Populations

This is an introductory/interdisciplinary (master's level) quantitative trait locus (QTL) mapping course. QTL mapping is associated with the statistical analysis of genetic/genomic data and is considered part of the general science known as bioinformatics. 

STAT 582 — Statistical Consulting And Collaboration

This course is designed to emphasize and develop the skills needed by a statistical consultant/collaborator. Topics include: problem-solving, consulting session management, written and oral communication, research ethics, design of experiments, collection of data, and application of statistical methods to real problems. 

ASM 540 — Geographic Information Systems Application

Fundamentals of GIS analysis applied to environmental, agricultural and engineering-related problems. Topics include data sources; spatial analysis; projections; creating data and metadata; and solving spatial problems.

CE 597 — Geospatial Modeling and Analysis

The course intends to enhance students’ fundamental knowledge and advanced skills in geospatial science and technology. It will be focused on quantitatively exploring and evaluating the patterns of both physical and social phenomena in spatial and temporal domains. Students will learn the analytics needed for mining and interpreting geospatial data.

CE 597 — Geographic Information Systems

Introduces the principles, methods and skills in GIS. Enhance capabilities in handling and analyzing geographic data. Conduct GIS spatial analysis tasks. Design and create a geographic database. Experience internet mapping. Advance skills with ArcGIS and extensions. 

CPB 619 — Design, Conduct and Analysis of Clinical Trials

This course reviews the various types of clinical trials that are used in medical research, e.g., therapeutic and preventive. The stages and activities of a “typical” trial are defined along with factors that influence study design. Key elements of data collection, organization, analysis, and interpretation, and reporting of results are discussed and illustrated using published reports of clinical trials. Differences in the design and conduct of trials in humans and animals will be considered including the ethical concerns and costs. A graduate level introductory course in biostatistics is highly recommended prior to this course.

CPB 625 — Clinical Biostatics

Intended for professional medical and biological science graduate students, this course is designed to familiarize them with the appropriate usage (and reporting) of different statistical tests in biomedical research. Students will be taught the basic theories underlying the different tests, the data assumptions underlying the application of those tests, and will review/critique published scientific articles that employed these tests. Students will have the opportunity to describe the appropriate statistical methods to be used in their proposed research and/or report the usage of appropriate statistical tests on their own data. 

CPB 626 — Design and Analysis of Epidemiologic Studies

Focuses on epidemiologic study design and the applications of statistical software to the analysis of data derived from health research. Includes an overview of epidemiologic study designs, frequency and association measures, generalized linear models, and survival analysis.

CPB 695 — Seminars in Epidemiology

Graduate students enrolled in this course are required to give a seminar presentation to the group, focusing on discussion of recent research in epidemiology, emphasizing study design, analysis and public health significance. Discussion topics will be selected from the following areas: clinical epidemiology, infectious disease epidemiology, chronic disease epidemiology, environmental epidemiology and spatial epidemiology. Active discussion of assigned readings is also expected each week. 

HDFS 629 — Family and Couple Interventions in Health Problems

This course addresses issues in behavioral and family interventions and research methodologies in health problems. Students will develop expertise in family and health theories and interventions, randomized clinical trial methodology, intervention research in healthcare settings, assessment of health and healthcare outcomes, and models of patient-centered care and adherence issues.

SOC 681 — Structural Equation Modeling

The course will introduce participants to structural equation models (SEMs) using AMOS. SEMs simultaneously model the measurement and conceptual structure of social phenomena and thus combine the strengths of factor analysis, path analysis, and simultaneous equation models. The course will be taught in the Social Research Institute Computer Laboratory. 

CSR 590 — Health Disparities in Vulnerable Populations Seminar

This intensive course will provide an introduction to the principles and practices of health disparities research. It will identify and explore the multifarious origins of health disparities in minority and vulnerable populations.

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