| STAT 4020. Statistics and Research Methodology |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: None |
| This
course introduces students to the basic methods of statistical analysis
and research methodology. Topics include descriptive and inferential
statistics, epidemiology, research designs, and reliability and
validity of measurement. Students will perform statistical analysis and
display of data and results, including use of a microcomputer software
package, and will critically evaluate published reports of clinical
and/or epidemiological studies. |
Grading System: ABCDF |
|
|
STAT 6300. Introduction to Epidemiology and Biostatistics |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: None |
| This
course serves as an introduction to epidemiology. Topics include basic
concepts, types of studies, description and analysis of epidemiologic
data, and epidemiology in disease control. This course introduces
students to basic biostatistical methods of descriptive and inferential
statistics. |
Grading System: ABCDF |
|
|
STAT 7010. Biostatistics I |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: None |
| This
course offers an introduction to the basic statistical techniques used
to analyze and interpret data in the health sciences and related
fields. Emphasis is on application of these methods, with the following
topics covered: graphical methods, probability, discrete and continuous
distribution, inferential statistics (estimation and hypothesis testing
for the one and two-sample case) for numeric and categorical data,
non-parametric methods, analysis of variance, regression, correlation
and critical reading of the research literature. |
Grading System: ABCDF |
|
|
STAT 7020. Biostatistics II |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: None |
| This
course is the second course in a two-course sequence in Biostatistics
that offers an introduction to some of the more advanced statistical
techniques used to analyze and interpret data in the health sciences
and related fields. Emphasis is on applications of these methods.
Topics include factorial ANOVA, multiple linear regression and
correlation, ANCOVA, logistic regression, longitudinal data analysis,
survival analysis, clinical trials, experimental design, epidemiology,
diagnostic tests, and critical reading of the research literature. |
Grading System: ABCDF |
|
|
STAT 7060. Research Design and Statistics |
0 lecture hrs | 0 lab hrs | 0 clinical hrs | 1 credit hrs |
| Prerequisite: None |
| The
primary objective of this course is to provide students with an
understanding of basic concepts and methods of statistical inference in
the biomedical health sciences. Upon completion of the course, students
should be able to understand, interpret, and critique the results of
application of statistical techniques as found in the health sciences
literature. This course is comprised of eight WebCT modules with
voice-overs and remote administration/testing capabilities. |
Grading System: S/U |
|
|
STAT 7070. Biomedical Statistics |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: None |
| This
survey course offers an introduction to the majority of statistical
techniques used to analyze and interpret data in the biomedical
sciences and related fields. Emphasis is on applications of these
methods, with the following topics covered: graphical methods,
probability, discrete and continuous distributions, inferential
statistics (estimation and hypothesis testing for the one and
two-sample case) for numeric and categorical data, non-parametric
methods, one-way ANOVA, simple linear regression, correlation,
factorial ANOVA (fixed and random effects), multiple linear regression
and correlation, ANCOVA, logistic regression, longitudinal data
analysis, survival analysis and the critical reading of the research
literature. |
Grading System: ABCDF |
|
| STAT 8110. Introduction to Biostatistics |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: Calculus |
| This course offers an introduction to the basic statistical techniques used to analyze and interpret data in the health sciences and related fields. Emphasis is on applications of these methods, with graphical statistics (estimation and hypothesis testing for the one and two-sample case) for numeric and categorical data, non-parametric methods, analysis of variance, regression, and correlation. |
Grading System: ABCDF |
|
STAT 8130. Introduction to Epidemiology
|
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: None |
| This course serves as an introduction to epidemiology. Topics include basic concepts, types of studies, description and analysis of epidemiologic data, and epidemiology in disease control. |
Grading System: ABCDF |
|
STAT 8240. Introduction to Clinical Trials
|
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: Introduction to Biostatistics – STAT 8110 or equivalent. |
| This introductory course will address basic statistical techniques used in clinical trials. Material presented will include the principles underlying the planning, management and implementation of clinical trials, the application of basic statistical methods used in the analysis of data from clinical trials, and the interpretation of results.
|
Grading System: ABCDF |
|
STAT 8260. Design and Analysis of Observational Studies
|
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8110. Introduction to Biostatistics (or equivalent) and STAT 8130.Introduction to Epidemiology (co-requisite). |
| Advantages and disadvantages of prospective and retrospective study designs; design and analysis issues in both cohort and case-control studies, including proper selection of study subjects, data quality, sources and types of bias, controlling for confounding, maximizing participation and minimizing loss to follow-up in prospective studies, power and sample size; statistical methods including categorical data analysis, logistic regression, Cox regression; use of statistical packages such as SAS and StatXact for analysis. Review and discussion of current representative studies. |
Grading System: ABCDF |
|
STAT 8270. Categorical Data Analysis
|
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: Introduction to Biostatistics - STAT 8110 or equivalent. |
| This course focuses on statistical methods for analyzing categorical data; topics include inference for a single proportion; inference for two-way contingency tables; models for categorical response variables, including logistic and loglinear models; analysis of matched-pairs data; power and sample size considerations. Emphasis will be placed on methods and models most useful in health-related research. |
Grading System: ABCDF |
|
STAT 8350. Epidemic Modeling
|
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: Introduction to Epidemiology - STAT 8130 and Categorical Data Analysis - STAT 8270. |
| This course serves as an introduction to types of epidemiological studies and covers modeling of various types of epidemics. |
Grading System: ABCDF |
|
STAT 8360. Systematic Reviews
|
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite:Introduction to Clinical Trials - STAT 8240. |
| This course covers systematic reviews of the literature for controlled clinical trails and observational studies. Statistical methods and computer software are reviewed and how to use systematic reviews in practice is detailed. Topics to be covered are Introduction to systematic reviews and meta analysis, systematic reviews of controlled clinical trails, investigating variability between studies, systematic reviews of observational studies, statistical methods and computer software, using systematic reviews in practice, the Cochrane collaboration, an other evidence-based medicine topics. |
Grading System: ABCDF |
|
| STAT 8370. Intermediate Epidemiology |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8110. Introduction to Biostatistics and STAT 8130.Introduction to Epidemiology, or permission of instructor. |
| Illustrates concepts, methods, and strategies used in epidemiology studies, beyond the principles discussed in Basic Epidemiology Courses. Topics include basic study designs, analysis of birth cohorts, measures of disease frequency and association, bias, confounding, effect modification and interaction, stratification and adjustment, quality control, and reporting of epidemiologic results. In the laboratory exercises, students work in small groups, further considering and discussing the topics and concepts covered in lectures. |
Grading System: ABCDF |
|
| STAT 8390. Seminar in Clinical and Translational Science |
2 lecture hrs | 0 lab hrs | 0 clinical hrs | 2 credit hrs |
| Prerequisite: Admission into the Graduate Program in Clinical and Translational Science. |
| This course consists of clinical and translational research seminars by GRU faculty members and visiting researchers. Students will have an opportunity to talk to each speaker informally and to serve as hosts to visiting scientists. |
Grading System: Satisfactory/Unsatisfactory |
|
| STAT 8400. Mentored Research in Clinical and Translational Science |
0 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: Permission of Clinical and Translational Science Program. |
| The student works closely with his/her faculty mentors and Advisory Committee on an in-depth study of a research question of interest to both student and mentors. The course may be repeated as necessary until the student completes the research. |
Grading System: Satisfactory/Unsatisfactory |
|
| STAT 8510. Programming for Data Analysis |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: None. |
| This course provides a hands-on exposure to programming, data management and report generation with one of the most popular statistical software packages. |
Grading System: ABCDF |
|
| STAT 8520. Statistical Theory I |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: Multivariable Calculus and Matrix Algebra. |
| Fundamentals of random variables and probability theory; discrete and continuous distributions; exponential families; joint, marginal, and conditional distributions; functions of random variables; transformation and change of variables; order statistics; convergence concepts; central limit theorem; sampling distributions. |
Grading System: ABCDF |
|
| STAT 8550. High Throughput Data Analysis |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8520 – Statistical Theory I. |
| This course will cover quality assessment and normalization of arrays; Summarization of various array-based assays: CGH, ChIP and methylation; Issues for high-throughput sequencing data; Tests of significance and multiple comparisons; Multivariate analysis of pathways and GO functional groups. |
Grading System: ABCDF |
|
| STAT 8610. Applied Linear Models I |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8110; Programming for Data Analysis – STAT 8510; Statistical Theory I – STAT 8520. |
| This course will continue the investigation of simple linear regression from the Introduction to Biostatistics course with extension to multiple linear regression models. Model selection, validation, diagnostics and remedial measures will be covered. SAS will be used for applying these methods to biomedical data. |
Grading System: ABCDF |
|
| STAT 8620. Statistical Theory II |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: Statistical Theory I – STAT 8520. |
| Point and interval estimation; hypothesis and significance testing; maximum likelihood and moment estimators; Bayes estimators; unbiased estimators; sufficiency and completeness; Fisher information; uniformly most powerful tests; likelihood ratio tests; asymptotic inference; introduction to Bayesian inference. |
Grading System: ABCDF |
|
| STAT 8630. Applied Linear Models |
3 lecture hr | 0 lab hr | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8110-Introduction to Biostatistics; STAT 8510-Programming for Data Analysis; STAT 8520-Statistical Theory I. |
| This course will cover simple linear regression with extension to multiple linear regression models including model selection, validation, diagnostics and remedial measures. Additionally, one-way analysis of variance (ANOVA), multiple treatment comparisons, factorial ANOVA, randomized complete-block designs, analysis of covariance (ANCOVA), ANOVA with unbalanced data, fixed-/random-/mixed-effect models, repeated-measures designs, and nested designs. SAS will be used for applying these methods to biomedical data. |
Grading System: ABCDF |
|
| STAT 8640. Generalized Linear Models I |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8630 – Applied Linear Models, STAT 8620- Statistical Theory II. |
| Fitting of generalized linear models, diagnostics, contingency tables, categorical data analysis, measures of agreement, asymptotic theory, overdispersion, quasi-likelihood, multicategorical responses, estimating equations, generalized linear mixed models. |
Grading System: ABCDF |
|
| STAT 8650. Introduction to Stochastic Processes |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8520 – Statistical Theory I. |
| Finite probability models, Markov chains, martingales, random walk, Poisson processes, modest elements of renewal and reliability theory, Brownian motion, stochastic differential equations. |
Grading System: ABCDF |
|
| STAT 8710. Applied Linear Models II |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8610. Linear Models I and STAT 8620. Statistical Theory II |
| One-way analysis of variance (ANOVA), multiple treatment comparisons, ANOVA diagnostics, factorial ANOVA, randomized complete block designs, analysis of covariance (ANCOVA), ANOVA with unbalanced data, random and mixed effect models, repeated measures designs, nested designs and response surface methods. |
Grading System: ABCDF |
|
| STAT 8720. Survival Analysis |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8610. Applied Linear Models I and STAT 8620. Statistical Theory I |
| This course serves as an introduction to time-to-event (survival) data analysis. Both theory and applications are covered and methods include non-parametric, parametric, and semi-parametric (Cox model) approaches. |
Grading System: ABCDF |
|
| STAT 8740. Design and Analysis of Clinical Trials |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8710. Applied Linear Models II and STAT 8240. Introduction to Clinical Trials |
| This course will address advanced statistical techniques used in the design and analysis of both clinical and sequential trials. |
Grading System: ABCDF |
|
| STAT 8750. Introduction to Genetic Analysis |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8630 – Applied Linear Models. |
| In addition to providing a basic introduction to genetics, this course also aims to connect fundamental principles of biology, genetics, and evolution to mathematical and statistical models used in genetic research. This course is a prerequisite for more advanced courses in statistical and population genetics (e.g. genetic analysis laboratory, statistical aspects of human population genetics, genetics in epidemiology, and theoretical basis of genetic analysis). By the end of the course, students are expected to have acquired a genetics vocabulary, to be familiar with single locus and multilocus inheritance, to have a broad understanding of the different types of genetic variation and how each could contribute to phenotypic variation in heritable traits, and most importantly, to have a basic understanding of how any of these concepts could be quantified in statistical and/or mathematical models. The course format will consist of lectures, discussions, and homework assignments. |
Grading System: ABCDF |
|
| STAT 8870. Biostatistical Consulting in Research |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8130. Introduction to Epidemiology; STAT 8610. Applied Linear Models I and STAT 8620. Statistical Theory II |
| This course is designed for students to gain practical experience in integration of statistical theory and application in current research, systematic formulation of problems, data format, collection procedures, design, analysis, interpretation and communication of results. A project write-up will be required at the conclusion of each project. |
Grading System: Satisfactory/Unsatisfactory |
|
| STAT 8880. Special Topics |
0 lecture hrs | 0 lab hrs | 0 clinical hrs | 1-3 credit hrs |
| Prerequisite: Permission of Instructor |
| This course is designed to cover special topics in the theory and methods of Biostatistics that are not covered in regular courses. The topics will depend on the research interests of the instructor and the students. |
Grading System: ABCDF |
|
| STAT 8890. Readings and Research |
0 lecture hrs | 0 lab hrs | 0 clinical hrs | 1 - 12 credit hrs |
| Prerequisite: Permission of the Instructor |
| This course consists of readings and research in the current biostatistical literature, advanced topics in biostatistical theory and methods, and a supervised research project which will potentially lead to publications and/or presentations. |
Grading System: Satisfactory/Unsatisfactory |
|
| STAT 8910. Biostatistical Consulting Project |
0 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 - 12 credit hrs |
| Prerequisite: Consent of Major Advisor. |
| Required course for Master of Science students who choose the Non-Thesis Option. Consists of one or more consulting project write-up(s), directed by a Biostatistics faculty member. A formal oral presentation is required at the conclusion of the consulting project(s). |
Grading System: Satisfactory/Unsatisfactory |
|
| STAT 8920. Thesis Research |
0 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 - 12 credit hrs |
| Prerequisite: Consent of Major Advisor. |
| The thesis project for the M.S. program will be of two types: (i) use of established but state-of-the-art statistical tools to analyze and report on collected data sets; or (ii) a rigorous review of statistical literature, possibly involving a small amount of methodological research, that has potential use in complex biomedical data analysis. |
Grading System: Satisfactory/Unsatisfactory |
|
| STAT 9110. Generalized Linear Models (GLMS) |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite:Applied Linear Models II - STAT 8710 and Categorical Data Analysis - STAT 8270. |
| Fitting of generalized linear models, diagnostics, asymptotic theory, overdispersion, quasi-likelihood, multicategorical responses, estimating equations, generalized linear mixed models. |
Grading System: ABCDF |
|
| STAT 9120. Theory of Linear Models |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8710. Applied Linear Models II. |
| This course is a study of the general linear statistical model. Topics include the analysis of linear models in univariate data, distributions of quadratic forms, full rank linear models and fixed effect models of less than full rank. Both balanced and unbalanced random and mixed models will also be covered. |
Grading System: ABCDF |
|
| STAT 9140. Generalized Linear Models II |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8640 – Generalized Linear Models I. |
| Fitting of Bayesian generalized linear models, contingency tables, and survival models, model reduction techniques, clustered and longitudinal data with vector-valued responses, homogeneity tests, measures of similarity. |
Grading System: ABCDF |
|
| STAT 9150. Advanced Statistical Methods in Genetic Analysis I |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: Computational Methods in Genomics & Genetics – GNMD 8050, Fundamentals of Genomic Medicine - SGSS 8092, Generalized Linear Models - STAT 9110. |
| Statistical methods for describing variation in qualitative and quantitative (disease) traits, including decomposition of trait variation into components representing genes, environment and gene-environment interaction. Topics include transmission of genes in populations, heritability, polygenic and multifactorial traits, complex segregations analysis, methods of mapping and characterizing simple and complex trait loci, pedigree analysis, variance components estimation, likelihood based and Bayesian interval mapping, epistasis, and use of public domain genetic analysis software. |
Grading System: ABCDF |
|
| STAT 9160. Analysis of Clustered and Correlated Data |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8710. Applied Linear Models II |
| Advanced topics in the analysis of clustered and correlated data, including correlation analysis, tests of correlation and covariance structure, repeated measures analysis, measures of agreement, and cluster-randomized trials. Instruction will be given in the proper use of software to carry out the analyses. Emphasis will be placed on methods and models most useful in clinical research. |
Grading System: ABCDF |
|
| STAT 9170. Advanced Computational Methods |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8610. Applied Linear Models I and STAT 8620. Statistical Theory II, or approval from course director. |
| This course covers modern computational issues important for implementing statistical methods that are not part of an existing statistical package. The methods covered are important for both method development and method implementation. As such, the course is designed for biostatistics students who want to focus on methods development or collaborative research, as well as for quantitative science students, such as in bioinformatics. |
Grading System: ABCDF |
|
| STAT 9220. Advanced Statistical Inference |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8620. Statistical Theory II |
| Families of models, likelihood, sufficiency, significance tests, compostie null and alternative hypotheses, similar regions, invariant test, interval estimation, point estimation, bias and variance, Cramer-Rao inequality, asymptotic theory, large-sample inference, likelihood ration test, score test, Wald’s test. |
Grading System: ABCDF |
|
| STAT 9230. Nonparametric and Robust Statistical Methods |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8620. Statistical Theory II and STAT 9170. Advanced Computational Methods |
| Non-parametric statistical methods, including rank-based methods for testing location and dispersion for one-, two-, and more than two-sample designs, as well as non-parametric measures of association; robust estimation methods, with emphasis on robust analogs of the mean, standard deviation, and third-moment skewness. Students will be introduced to non-parametric resampling techniques (bootstrapping and permutation methods), which will be used with robust estimation to test hypotheses. Extensive use of computer-intensive estimation and hypothesis testing procedures. |
Grading System: ABCDF |
|
| STAT 9240. Bayesian Inference |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8620 – Statistical Theory II. |
| This course introduces Bayesian methods for statistical inferences. We will look over concepts of Bayesian theories and Bayesian computational tools. Mainly we will follow up Markov chain Monte Carlo (MCMC) sampling methods including independent samplings (rejection sampling, importance sampling) and dependent samplings (Metropolis-Hastings, Gibbs sampling, slice sampling, and sequential Monte Carlo methods). These MCMC methods can be utilized in the final project. |
Grading System: ABCDF |
|
| STAT 9250. Advanced Statistical Methods in Genetic Analysis II |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 9150. Advanced Statistical Methods in Genetic Analysis I and STAT 9170. Advanced Computational Methods |
| Rigorous statistical and computational treatment of methods for localizing genes and environmental effects involved in the etiology of complex human traits using casecontrol and family data. Topics include theory of association and linkage disequilibrium mapping, candidate gene and genome-wide association mapping, detecting and accounting for population structure and admixture, analysis of dense SNPs maps, haplotype blocks, and graphical models. |
Grading System: ABCDF |
|
| STAT 9270. Computational Genomics & Proteomics |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: GNMD 8050. Computational Methods in Genomics & Genetics; SGSS 8092. Fundamentals of Genomic Medicine; STAT 9110. Generalized Linear Models |
| Computational inference and visualization approaches for high-throughput data from genomics and proteomics. Topics include an introduction to high-throughput experimental data, experiment planning, data normalization, data representation, clustering, classification, approaches for detecting differential expression, hierarchical Bayesian models, Bayesian variable selection, other computational approaches to variable selection, statistical network models, and statistical metrics for model validation. |
Grading System: ABCDF |
|
| STAT 9280. Advanced Special Topics |
3 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 credit hrs |
| Prerequisite: STAT 8710. Applied Linear Models II and permission of instructor. |
| This course is designed to cover advanced topics in the theory and methods of biostatistics, clinical trials, epidemiology, statistical and quantitative genetics, and other areas that are not covered in existing courses. The topics will depend on the research interests of the instructor and the students. |
Grading System: ABCDF |
|
| STAT 9300. Dissertation Research |
0 lecture hrs | 0 lab hrs | 0 clinical hrs | 3 - 12 credit hrs |
| Prerequisite: Admission to Ph.D. candidacy and permission of Major Advisor. |
| The student works closely with his/her faculty mentor on an in-depth study of a research question of interest to both student and advisor. The course may be repeated as necessary until the student completes the research. |
Grading System: Satisfactory/Unsatisfactory |
|
