Program in Epidemiology and Biostatistcs

Certificate in Biostatistics

A joint graduate certificate from the Department of Statistics in the Volgenau School of Information Technology & Engineering and the Department of Global and Community Health, College of Health and Human Services.

Points of Contact

Heibatollah Baghi, PhD
Phone: 703-993-4677
Email:hbaghi@gmu.edu
Fax: (703) 993-1908

William Rosenberger, PhD
Phone: 703-993-3645
Email: wrosenbe@gmu.edu
Fax: (703) 993-1700

About the Program

Present day health care organizations rely on thorough analysis of health-related information and expect that its personnel be competent in up-to-date statistical procedures. Aiming at meeting those requirements, this certificate prepares participants to apply statistical methods to data analysis of health care issues. It is directed at students in health-related disciplines, health scientists and professionals in government agencies (such as the National Institute of Health), pharmaceutical companies, hospitals, public health agencies and other organizations involved in designing, analyzing, and interpreting increasingly complex health-care data.

Admission Requirements

Applicants must hold a bachelor’s degree from a regionally accredited institution of higher education in a discipline related to health science or statistics, with a GPA of 3.00 in the last 60 credits. Such fields include medicine, biology, nursing, health science, biostatistics, statistics, mathematics, and psychology. A course in statistics and a course in college algebra with a grade of B or higher is required for admission to the program.
Application to this Certificate is made either through the Volgenau School of Information Technology & Engineering or through the College of Health and Human Services.

Program Requirements

A minimum of 15 credits is required for the completion of this certificate. The students are expected to complete one course from each of the five groups.

STAT 535 or STAT 554
STAT 660
GCH/NURS 804 or STAT 656 or STAT 668
GCH/NURS 805 or STAT 662
GCH 712

A maximum of 3 credits in equivalent coursework taken at another college or university can be applied towards the certificate. Grades of B or better are required for transfer of courses. All decisions as to equivalency will be made by the corresponding department (Department of Statistics for STAT courses and Global and Community Health for GCH courses). A minimum GPA of 3.0 in courses taken at GMU is required for the certificate and at most one course with a grade of B or less can be applied.

Courses

STAT 535 Analysis of Experimental Data (3:3:0)
- Prerequisite: STAT/IT 250 or equivalent. Statistical methods for analysis of experimental data, including ANOVA and regression. Parametric and nonparametric inference methods appropriate for a variety of experimental designs are presented along with use of appropriate statistical software. Intended primarily for researchers in the natural sciences. Can be used to satisfy requirements for certificate in federal statistics, but not MS in statistical science. Certificate program students granted credit for only one of STAT 510, 535, or 554.
STAT 554 Applied Statistics (3:3:0)
- Prerequisite: STAT 344 or equivalent, or permission of instructor. Application of basic statistical techniques. Focus is on the problem (data analysis) rather than on the theory. Topics include one and two sample tests and confidence intervals for means and medians, descriptive statistics, goodness-of-fit tests, one- and two-way ANOVA, simultaneous inference, testing variances, regression analysis, and categorical data analysis. Normal theory is introduced first with discussion of what happens when assumptions break down. Alternative robust and nonparametric techniques are presented. Certificate program students granted credit for only one of STAT 510, 535, or 554.
STAT 656 Regression Analysis (3:3:0)
- Prerequisites: STAT 554, 501 or permission of instructor and matrix algebra. Simple and multiple linear regression, polynomial regression, general linear models, subset selection, step-wise regression, and model selection. Also covered are multicollinearity, diagnostics, and model building. Both the theory and practice of regression analysis are covered.
STAT 660 Biostatistical Methods (3:3:0)
- Prerequisites: STAT 535 or 554 and a working knowledge of a statistical software package SAS or SPSS.Statistical methods essential to the analysis of rates and proportions from data associated with clinical trials, case-control, prospective and cross-sectional studies in the health care sector. Risk assessment as measured by relative risks and odds ratios are central concepts. Construction and interpretation of logistic regression models for binary and polytomous responses. Poisson regression models for the analysis of rates. Concepts are applied to the analysis of real data from major medical studies using statistical software packages such as SAS, SPSS, and StatExact.
STAT 662 Multivariate Statistical Methods (3:3:0)
- Prerequisite: STAT 554 or equivalent and STAT 501, or permission of instructor. Standard techniques of applied multivariate analysis. Topics include review of matrices, Tsquare tests, principle components, multiple regression and general linear models, analysis of variance and covariance, multivariate ANOVA, canonical correlation, discriminant analysis, classification, factor analysis, clustering, and multidimensional scaling. Computer implementation via a statistical package is an integral part of the course.
STAT 668 Survival Analysis (3:3:0)
- Prerequisites: STAT 544, 554 or 535, and STAT 501 or a working knowledge of SAS. Survival Analysis is a class of statistical methods for studying the occurrence and timing of events. In medical research, the events may be deaths, and the objective is to determine factors affecting survival times of patients following treatment, usually in the setting of clinical trials. Methods can also be applied to the social and natural sciences and engineering where they are known by other names (reliability, event history analysis). Concepts of censored data, time-dependent variables, and survivor and hazard functions are central. Nonparametric methods for comparing two or more groups of survival data are studied. The Cox regression model (proportional hazards model), Weibull model, and the accelerated failure time model are studied in detail. Concepts are applied to analysis of real data from major medical studies using SAS software.
GCH/NURS 804
- Advanced Quantitative Data Analysis for Healthcare Research I (3:3:0) Prerequisite: A graduate-level course in statistics. Examines factorial ANOVA, factorial ANCOVA, repeated measures ANOVA, ANOVA and ANCOVA via regression approach, and mutiway frequency analysis. Students apply mathematical calculations and interpret SPSS outputs using healthcare research data.
GCH/NURS 805 Advanced Quantitative Data Analysis for Healthcare Research II (3:3:0)
- Prerequisite: GCH/NURS 804 or equivalent statistics course. Examines multivariate analysis of variance (MANOVA) multivariate analysis of covariance (MANCOVA), and multiple regression (ordinary least squares) and logistic regression. Students apply mathematical calculations and utilize linear combinations for multivariate tests in healthcare research.
GCH 712 Introduction to Epidemiology (3:3:0).
- Provides an introduction to epidemiology as a body of knowledge and a method for analyzing health problems. Uses analytic methods to critically appraise existing literature, design and implement evaluation processes, collect and analyze data, and apply revenant findings to the practice environment. Course Website.
Total = 15 Credits

Explanation of Credit Hours
Course titles are followed by numbers in parentheses (0:0:0), separated by colons. The numbers have the following significance:

First Number: credits for the course
Second Number: hours of lecture or seminar per week for the course
Third Number: hours of laboratory for the course

For independent study, readings, topics, or similar courses, individual instructors set hours.