MUSC Bulletin | College of Graduate Studies
Department of Public Health Sciences
Requirements for Admission
The Department of Public Health Sciences offers graduate study leading to the Master’s of Science in Biomedical Sciences or Doctor of Philosophy in Biomedical Sciences. Applicants must demonstrate an interest and aptitude in the development or application of quantitative techniques to problems in biomedicine. In addition to fulfilling general admissions guidelines of the College of Graduate Studies, students are encouraged to complete some undergraduate training in biology and mathematics. Calculus I and II are required coursework and knowledge of linear algebra is beneficial.
The Department of Public Health Sciences creates an environment that continually provides opportunities and challenges for novel and creative approaches and solutions to biomedical research. The faculty with diverse backgrounds and expertise in biostatistics, epidemiology, and behavioral and social sciences provides a synergistic environment for students to actively pursue cross-disciplinary methodological and applied research in public health.
The graduate program in biostatistics provides in-depth training in statistical methods and their application in medical and health-related fields. Areas of particular emphasis are clinical trials, categorical, multivariate, and longitudinal data analysis methods, survival analysis, recurrent events, multiple outcomes, and Bayesian biostatistical methods.
The program in epidemiology emphasizes the identification and study of factors leading to disease and disability, with the ultimate goal of prevention and control. These factors affecting disease etiology include environmental exposures as well as life-style risks with possible genetic interactions. The research studies in epidemiology include health outcome data, and when possible, measurements of biological markers of exposure, effect and susceptibility. Areas of particular emphasis: cancer, cardiovascular disease, perinatal/reproductive health, infectious disease, neurological disorder, tobacco and other substance use.
After mastering the basic concepts of biostatistics and epidemiology, the student becomes involved in quantitative design and analysis of clinical or basic science research, from which an independent research topic is selected for a thesis or dissertation project. Graduates from the department work in a variety of academic institutions, governmental agencies like the National Institutes of Health, the Centers for Disease Control, and the Environmental Protection Agency, or in pharmaceutical or health care industries.
Master of Science in Biomedical Sciences
The course program leading to this Master’s degree normally requires two years of full-time study. Graduation requirements include a minimum of 38 credit hours of didactic work, a successful qualification examination, and the completion of a thesis, which demonstrates the student’s mastery of biostatistical or epidemiological, methods in the analysis of a biomedical problem.
Doctor of Philosophy
In addition to core and elective course work, the student is expected to participate in the collaborative and teaching programs of the Department. Two examinations and a dissertation will demonstrate that the student has not only mastered the theoretical and applied aspects of biostatistics or epidemiology, but that s/he can apply the methods in a novel fashion to a relevant biomedical problem. The program includes 60 didactic credit hours in biostatistics and 45 in epidemiology, some of which are taken outside the division and some of which may transfer from a previous Master’s degree program.
Faculty Research Interests
Faculty research interests can be found on the web at http://academicdepartments.musc.edu/phs.
BMTRY-700. Biostatistics Methods I: Introduction to Clinical Biostatistics. This course introduces basic applied descriptive and inferential statistics. Topics include elementary probability concepts, an introduction to statistical distributions, point and interval estimation, hypothesis testing, and simple linear regression and correlation. Basic data management and analysis techniques will be introduced using the SAS system for personal computers. (includes 1 cr laboratory session) Prerequisites: College Algebra & at least one course in Calculus. 5 s.h.
BMTRY-701. Biostatistics Methods II: Regression Methods in Biology and Medicine. The objective of this course is to provide basic and intermediate skills necessary to apply regression methods to clinical and basic science research data. Topics include regression issues such as least squares estimation, hypothesis testing, diagnostics, model building and variable selection, and indicator variables. Simple and multiple linear regression, logistic regression, Poisson regression, and modeling of time-to-event (survival) data will be covered. The course uses a problem-based approach and applications to clinical and basic science problems are provided. Prerequisites: BMTRY 700. 4 s.h.
BMTRY-702. Biostatistics Methods III. The course covers a variety of intermediate level topics required to complete core competencies for analysis and interpretation of clinical and basic science data. The course emphasizes experimental designs employed in biological and medical research, including randomized block and nested designs, and factorial experiments. Longitudinal data methods including random and mixed effects models, and missing data methods are covered. Prerequisites: BMTRY 701. 4 s.h.
BMTRY-706 Theoretical Foundations of Statistics I. This course covers basic probability theory, random variables, transformation of random variables, expectation, moments and moment generating functions, discrete and continuous probability distribution functions; joint, marginal, and conditional distribution functions, bivariate normal distribution, and inequalities. Prerequisites: concurrent 700. 3 s.h.
BMTRY 707. Theoretical Foundations of Statistics II. This course is the continuation of Theoretical Foundations of Statistics I. Topics covered are order statistics, stochastic convergence, point and interval estimation, hypothesis testing, evaluation of estimates and tests, and asymptotic theory. Prerequisites: BMTRY 700, 706. 3 s.h.
BMTRY-711. Analysis of Categorical Data. This course offers a short review of standard measures of association and chi-square methods for binomial and multinomial distributions, followed by several special-purpose two-dimensional techniques. Other areas covered include the development of maximum likelihood-based inference (unconditional and conditional) for categorical data using generalized linear models. Models for binomial, multinomial and count data will be examined. In addition, topics including log-linear models, analysis of three-dimensional and higher tables, model selection strategies, regression model diagnostics, analysis of correlated or matched data, and generalized estimating equations, will be covered. Prerequisites: BMTRY 700, 706, 701. 3 s.h.
BMTRY-713 Infectious Disease Epidemiology. This course provides an overview of infectious disease epidemiology with an emphasis on the application of epidemiologic techniques to a variety of diseases. Lectures, supplemented by video presentations and case studies provide the framework for the course. Prerequisite BMTRY 736 or permission of the instructor. Fall & Spring. 3 s.h.
BMTRY-714. Linear Models in Biology and Medicine. The matrix representation of the general linear statistical model is studied through the implication, distribution, and partitioning of quadratic forms and their probability distributions. Estimation of parameters in the linear model by methods of maximum likelihood and least squares will be presented along with the accuracy and precision of these estimators. Estimability in both the full rank and less than full rank models is introduced. The test statistic for the general linear hypothesis is derived, and its distribution is determined under an assumption of normally distributed errors for both the null and a general alternative hypothesis. Sufficient examples are given to show its application to tests on means as well as in ANOVA and ANOCOVA. Students prepared in basic statistical methods and theory, and matrix algebra are eligible to take this course. Prerequisites: BMTRY 701, 707. 3 s.h.
BMTRY-717. Statistical Methods for Clinical Trials. This course is intended mainly for MS and PhD students in DBE interested in the statistical methods and issues arising in a variety of clinical trials. The course will include topics in adaptive/flexible study design, adaptive randomization, sample size estimation, missing data handling, interim analysis methods, and issues in data analysis. The course will also cover topics related to the statistician's role in clinical trials, including the presentation of statistical information and statistical monitoring of safety data. At the completion of the course, students will have the tools to collaborate with clinicians in the design and implementation of clinical trials as well as analysis of study data, and will have developed their skills in being more critical readers of the medical literature. Prerequisites: BMTRY 700, 701, 702, 722, 724. 2 s.h.
BMTRY-719. Bayesian Biostatistics. It is a graduate course on effective and sophisticated approaches to Bayesian modeling and computation in biostatistics and related fields. The course begins with a gentle introduction of Bayesian inference starting from first principle, but it intends to cover the philosophical backgrounds, logical developments and computational tools associated with Bayesian. Prerequisites: 701 707. 3 s.h.
BMTRY-722. Analysis of Survival Data. This is an introductory course in theory and application of analytic methods for time-to-event data. The methods covered include nonparametric, parametric, and semi-parametric (Cox model) approaches. The topics covered will also include types of censoring and truncation, sample size and power estimation, and a brief introduction to counting process method. Extensive use of SAS procedures for survival analysis is incorporated into the course. Prerequisites: BMTRY 701, 706, and working knowledge of SAS. 3 s.h.
BMTRY-724. Design and Conduct of Clinical Trials.This is a comprehensive course providing an overview in the design and conduct of clinical trials. The course covers the types of clinical trials; study design (including sample size estimation); randomization methods and implementation; project and data management; ethics; and issues in data analysis (e.g., intent-to-treat; handling of missing data ; interim analyses). The course is designed primarily for the students in the Department of Biostatistics, Bioinformatics, and Epidemiology; however, both clinical and basic science investigators can benefit from this course provided they have the required background in basic statistics. Prerequisites: BMTRY 700. 3 s.h.
BMTRY-726. Multivariate Methods in Biology and Medicine. This course will consist of multivariate techniques in biology and medicine including multivariate tests of mean vectors and covariance matrices, multivariate analysis of variance and regression, repeated measures analysis, random and mixed effects models, generalized estimating equations, generalized linear mixed models, canonical correlation, factor analysis, principal components analysis, discriminant analysis. Directed to biostatistics students; useful for epidemiology students. Prerequisites: BMTRY 702, 706, Knowledge of Matrix Algebra & SAS. 3 s.h.
BMTRY-734. Cancer Epidemiology. This survey course will introduce students to the major cancer risk factors. For the major cancers the most important epidemiological studies will be reviewed. The issue of genetic susceptibility and the use of biomarkers in cancer epidemiology will be studied as well as cancer screening. Prerequisites: BMTRY 736 or permission of the instructor. 3 s.h.
BMTRY-736. Foundations of Epidemiology (Epidemiology I). This course provides an introduction to basic epidemiologic principles including measurements of disease occurrence, study designs (cohort, case-control, randomized clinical trials) and calculation of risk. Lecture material is supplemented with exercises and discussion of examples from the epidemiologic literature and presentations of epidemiologic studies by guest speakers. Prerequisites: None. 3 s.h.
BMTRY-737. Epidemiology of Cardiovascular Diseases. This is an advanced course designed to acquaint students with the use of epidemiology in the study and investigation of cardiovascular diseases. Prerequisites: BMTRY 736 or permission of instructor. 3 s.h.
BMTRY-738. Design and Conduct of Epidemiologic Studies. An emphasis will be placed on procedures used in the implementation of epidemiological research studies. Prerequisites: BMTRY 736 or permission of instructor. 3 s.h.
BMTRY-747. Foundations of Epidemiology II. This course will provide a comprehensive and quantitative view of the design, conduct, analysis, and interpretation of epidemiological studies and use of EGRET software. There is a more in-depth coverage of topics than in Epi I. Prerequisites: BMTRY 700, 701 concurrently. 3 s.h.
BMTRY-748. Foundations of Epidemiology III. This course will provide an in-depth quantitative view of advanced statistical analysis of epidemiological studies. The use of epidemiological analysis software (Epicure) will be taught. Builds on techniques developed in Epi II. Prerequisites: BMTRY 701, 747. 3 s.h.
BMTRY-763. Spatial Epidemiology-Statistical Methods & Appl. This course focuses on the basic epidemilogical and statistical issues to be found int he study of the spatial/geographical distribution of disease. The topics of disease mapping, disease clustering and ecological analysis will be examined. Prerequisites: BMTRY 701. 3 s.h.
BMTRY-764. Statistical Computing for Research. Student learn to use the primary statistical software packages (SAS, R, Stata), principles of data management, and scientific document preparation. Prerequisites: BMTRY 700. 3 s.h.
BMTRY-781. Methods in Clinical Cancer Research. This clinical cancer research training course will follow the general curriculum of the AACR/ASCO Methods in Clinical Cancer Research with greater attention given to trial design and statistical issues.. Didactic lectures will cover the following areas: (1) clinical and statistical design of phase I, II and III trails; (2) incorporation of correlative and biomarkers in clinical trials, (3) considerations in chemotherapy, surgery, radiation and multimodality trials, (4) quality of life and other patient reported outcomes in cancer research, (5) the protocol review and IRB process, (6) informed consent, (7) data collection, trial monitoring and investigator responsibilities, (8) the grants process and mentoring. Other topics are incorporated as well, (e.g., disparities research). In addition to the didactic portions of the training, each trainee will have a clinical research proposal which will be developed into a "letter of intent" (LOI) for a clinical trial. For students in the Paul Calabrese Translational Clinical and Translational Oncology K-12 training program, the LOI will be used during the latter part of the students' training and will be further developed (with a mentorship team) into a protocol to be submitted to HCC. In addition to the didactic sessions, other contact hours will take the form of a journal club where clinical research papers from journals such as Clinical Cancer Research or Journal of Clinical Oncology are discussed, and protocols that are being undertaken at HCC are reviewed and discussed. Lastly, trainees will also be required to attend and take part in the HCC Protocol Review Committee's monthly meetings (meetings occur every 3 weeks). This will allow the trainees to be exposed to a variety of studies ranging from Phase I to III cancer trials, in addition to observational, translational and qualitative research studies. Trainees will also be encouraged to attend one or more of the HCC Data Safety and Monitoring Board meetings to gain exposure to issues of trial review and monitoring.
|Last Published with Edits:||September 19, 2014 9:04 AM|
|Last Comprehensive Review:||Fall 2012|