This course is designed to examine assessment strategies and intervention tools for students experiencing problems in acquiring literacy skills. 30 hours of field experience is required.
An introductory course in mild disabilities which covers definitions, historical development of the area of mild disabilities as a field of study, and major contributors; various theories and philosophies affecting the field; and current trends and issues. 30 hours of field experience is required. Prerequisite(s): admission to graduate program.
An introductory course in mild disabilities which covers materials methods, definitions, usage, and development of methods for teaching students with mild disabilities, and major contributors; theories and philosophies affecting this area of study, especially inclusion; and current trends and issues. 30 hours of field experience is required. Prerequisite(s): admission to graduate
program.
SPED 6011 Language Development and Communication Disorders (3-0-3)Students will study how language typically develops, theories related to etiology of language disorders, and the effects of language disorders on functioning for children with disabilities. Students will learn approaches for remediating language
disabilities and will apply them in authentic settings. 30 hours of field experience is required. Prerequisite(s): admission to graduate program.
An intensive practicum course designed for MAT candidates in Special Education. Candidates will be placed under the supervision of a mentor teacher if they are not currently teaching. A mentor teacher and university faculty member will work to support the candidate’s practicum. The candidate will gain first-hand experiences working with students in an inclusive public or private school class with an emphasis on planning, reflecting, and refining teaching practices. Candidates must submit an electronic portfolio for review. Prerequisite(s): Successful completion of at least 30 semester hours of program requirements. The practicum requires 75 consecutive days of field experience.
Grade Mode: Normal, Audit
Prerequisites: PREREQUISITES: SPED6009, SPED6010, SPED6014, SPED6015 Pre-Req Min Grade: C, C, C, C
An overview of the field of severe disabilities; includes historical, legal, philosophical, ethical, and programming issues; current trends and issues in the field. Students will have in-depth coverage of current issues in the field of severe disabilities (including autism spectrum disorders, traumatic brain injury, moderate to severe mental retardation, and orthopedic disabilities). Current perspectives in educational programming will be covered. 30 hours of field experience is required. Prerequisite(s): admission to graduate program.
Methods for teaching students with moderate to severe disabilities throughout the life span including hands-on experiences;emphasis on career education, transitions, and lesson plans emphasizing life skills. Emphasis is on self-evaluation and plans to improve instruction. 30 hours of field experience is required. Prerequisite(s): admission to graduate program and SPED 6014.
Grade Mode: Normal, Audit
Prerequisites: PREREQUISITES: SPED6014 Pre-Req Min Grade: C
Single subject research is an experimentally controlled method for evaluating effects of interventions on participant responding. Unlike group designed research, single subejct studies compare participant response within and across baseline and intervention conditions using repeated measures over time. The course emphasizes the evaluation of quality, published single subject research and the design of single subject studies. Candidates will document at least 30 hours of field experience in which they apply skills learned in SPED 6204.
This is the culminating graduate course in the M.Ed. Special Education program. In this course an electronic portfolio and graduate research project are produced. It incorporates a competency based research-to-practice project using data-based
strategies and interventions in a special education applied setting. 30 hours of field experience is required. Grading is on the A, B, C, D, F, WF, etc. scale.
This course provides an in-depth study of the cross-categorical model of service delivery for students with emotional/behavioral disorders, mild intellectual disabilities, and learning disabilities. Emphasis is on the similarities and differences of the three categories in historical treatment, definition, characteristics, incidence, prevalence etiology, and implications for teaching. Current issues and trends in special education will be studied.
This course examines problems inn the light of recent knowledge and research in special education. The focus is on specifically designated areas of special education. 30 hours of field experience is required.
This course is designed to provide a forum for learning and applying principles of applied behavior analysis. Students will read current research related to positive behavior change using database searches and required readings. Students will demonstrate knowledge and skills by completing multiple intervention projects that include objective assessment, analysis of potential interventions identified in peer reviewed research, and data collection for baseline, intervention, and generalization over the course of the semester. These intervention projects will address problems in areas of communication, social, and academic
functioning of children with disabilities.
Grade Mode: Normal, Audit
Prerequisites: PREREQUISITES: SPED6003 Pre-Req Min Grade: C
This course is designed to provide special educators with an intensive study of the CEC Code of Ethics and Standards for professional practice. Students will analyze the role of professional standards as they impact Special Educators throughout
their careers. Students will examine current issues in the field and will gain in depth knowledge through coursework and an applied research project. Prerequisite(s): Enrollment in the Educational Specialist program.
SPED 7024 is an advanced course in grant and technical writing for future education specialists which includes: (a) analysis of the components of educational grants, (b) systematic instruction and practice in grant writing, (c) analysis and examples of successful grants, (d) grant writing methodologies, strategies, and techniques, (e) in-depth analysis and practice in the research process, (f) detailed instruction in APA-format and dissemination of research results, and (g) completion of a grantwriting project.
This course is designed as part of the exit requirements in the Educational Specialist in Special Education program. In this course a graduate research project will be produced. It incorporates a competency-based research-to-practice project using data-based strategies and interventions in a special applied setting. 20 hours of field experience is required. Must be completed in the student’s final semester. Prerequisite(s): Enrollment in the Educational Specialist program.
SPED 7026 includes readings, lecture, discussion, and assignments designed to familiarize candidates with a wide variety of issues related to providing services for students with autism. The course provides experienced teachers an introduction to theory and practice working with individuals with autism. The first half of SPED 7026 is designed to give candidates a historical perspective on the development of the field of autism theory and intervention. The second half of the course uses knowledge about autism theory to study multiple intervention approaches for teaching children with autism. Candidates will assess needs,
choose appropriate strategies, implement strategies, and objectively evaluate results for individual students with autism in authentic settings. Prerequisite(s): Enrollment in the Educational Specialist program.
A course for educators and administrators who work with students with disabilities which includes an overview of transition history and development in the field of special education, discussion of research and best practice in transition policy, interagency collaboration and community-based instruction, methods for improving transition outcomes for students with disabilities ranging from mild to severe, and current issues in transition policy and legislation in special education.
SPED 7028 is an advanced course in the assessment and direct instruction of students with learning difficulties. This course is designed for future education specialists and emphasizes: (a) the components of direct instruction, (b) systematic
analysis of direct instruction techniques in language, reading, writing, math, & behavior, (c) assessment of student response, (d) direct instruction methodologies, strategies, and techniques and troubleshooting, (e) formal & informal assessment, (f) linking information gathering to classroom decision making, and (g) adapting instruction to improve outcomes. Prerequisite(s):
Enrollment in the Educational Specialist program.
This course is designed for the in-service teacher who is at the post masters’ level; it provides an in-depth study of the cross-categorical model of service delivery for students with emotional/behavioral disorders, mild intellectual disabilities, and
learning disabilities. Emphasis is on the similarities and differences of the three categories in historical treatment, definition, characteristics, incidence, prevalence etiology, and implications for teaching. Current issues and trends in special education will be studied.
This practicum is for the master teacher to demonstrate competence in the inter-related special education classroom. This practicum will represent a synthesis of knowledge being put into practice at the Specialist’s level.
This course is designed for the in-service teacher who is at the post-masters’ level; a school/community project involving a model of teaching, in-service training sessions, or innovative practice in the field of special education will be designed, implemented, and evaluated by the student under the supervision of the major professor. The course emphasizes state of the art methods for assessing/teaching students in interrelated classrooms.
A course for educators in special or general education which will cover: the history and development of special education as a civil rights initiative; current legislation, policies, procedures, and regulations governing special education practice; an
examination of the Individuals with Disabilities Education Improvement Act (IDEA); current issues and controversies regarding inclusion and collaborative teaching; and accountability and assessment for students with disabilities. Prerequisite(s): Enrollment in the Educational Specialist program
This course serves as an introduction to epidemiology and biostatistics. The epidemiology portion of this course is intended to introduce students to epidemiology and its application to public health research and practice. It provides a conceptual foundation for further study of epidemiology; especially study design, quantitative concepts and methods, analysis, and interpretation. The biostatistics portion of this course offers an introduction to the basic statistical techniques used to analyze and interpret data in the biomedical, health sciences and related fields. Emphasis is on applications of these methods, with probability, discrete and continuous distribution, inferential statistics (estimation and hypothesis testing) for numeric and categorical data, non-parametric methods, analysis of variance, regression, and correlation topics covered.
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 numeric and categorical data, non-parametric methods, analysis of variance, regression, correlation and critical reading of the research literature.
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester Degree Restrictions: Doctor of Philosophy, Master of Public Health, Master of Science
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.
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
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, and survival analysis and the critical reading of the research literature. Prerequisites: College Algebra (Calculus highly recommended) or prior approval of course director.
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
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 modules with voice-overs and remote administration/testing.
Prerequisites: College algebra or permission of the instructor.
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.
Prerequisites: College algebra or permission of the instructor.
This course offers an introduction to the basic statistical techniques used to analyze and interpret data in the biomedical, health sciences and related fields. Emphasis is on applications of these methods, with graphical methods, probability, discrete and continuous distributions, inferential statistics (estimation and hypothesis testing) for numeric and categorical data, non-parametric methods, analysis of variance, regression, and correlation. Students will learn to use the NCSS microcomputer statistical software package.
Grade Mode: Normal, Audit
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
Prerequisites: 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 categorical data, non-parametric methods, analysis of variance, regression, and correlation.
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
This course covers basic probability theory, the concepts of random variables, univariate and multivariate distributions, discrete and continuous joint, marginal, and conditional distributions in general. Several specific probability distributions are covered in detail: normal, binomial, multinomial, Student’s t, F, chi-square. Expectation theorems, the law of large numbers, and the central limit theorem are also covered.
Prerequisites: Calculus.
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
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 provides a hands-on exposure to programming, data management and report generation with one of the most popular statistical software packages.
Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
This course is a study of the general linear statistical model and the linear hypothesis. Topics include the multivariate normal distributions of quadratic forms, and parameter estimation and hypothesis testing for full-rank regression models. Variable selection, regression diagnostics and “dummy” variable coding will also be covered.
Prerequisites: Knowledge of linear algebra.
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
Prerequisites: Multivariable Calculus and Probability & Distributions. STAT 8120.
Introduction to the theoretical properties of point estimators and tests of hypotheses. Sufficient statistics, likelihood, best linear unbiased estimates, elements of statistical tests, the Neyman-Pearson Lemma, UMP tests, univariate normal inference, decision theory and multivariate distributions are covered.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8120 Pre-Req Min Grade: C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
This course covers the basic principles of experimental design. It covers the concepts of randomization, blocking, replication and interaction. Various designs are covered and their strengths and weaknesses are illuminated. These designs include factorials, complete and incomplete designs, Latin and Greco-Latin square designs, and split-plot designs. Confounding and fractional replication is also covered.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8110 Pre-Req Min Grade: C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
This introductory course will address basic and advanced 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.
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.
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.
This course is a continuation of Linear Models I, and covers the analysis of experiments using linear models. Single- and multiple-factor analysis of variance and analysis of covariance will be examined, including types of factor effects and analysis involving missing data. Topics of experimental design relevant to biomedical research will also be covered.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8210 Pre-Req Min Grade: C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
This course introduces students to the basics of demographic estimation and analysis and introduces students to those statistical methods useful in the analysis of rates and proportions.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8130 Pre-Req Min Grade: C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
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.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8110, STAT8120, STAT8220 Pre-Req Min Grade: C, C, C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
Introduction to modeling DNA and protein evolution and to reconstructing evolutionary relationships from DNA and protein sequences. Statistical models are applied to comparisons of DNA and protein sequences to make inferences about their common ancestry and past evolutionary events.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8120, STAT8220 Pre-Req Min Grade: C, C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
This course is designed to cover special topics in theory and methods of Biostatistics that are not covered in regular courses. The topics will depend on the research interest of the instructor and the students. Prerequisites: Permission of the Instructor.
Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
The analysis of frequencies of single Mendelian genes within populations including Hardy-Weinberg equilibrium, non-random mating, admixture/subdivision, linkage equilibrium, selection/mutation, likelihood estimation, latent variables and the EM algorithm, pedigree analysis and genetic identify, linkage analysis.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8220 Pre-Req Min Grade: C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
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.
Prerequisite: Permission of instructor.
Grade Mode: Satisfactory, Audit
Credit Hours: 1 to 6 Contact Hours: 1 to 6 Lecture Hours: 1 to 6 Lab Hours: 0 Other Hours: 0
Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
Schedule Type: Independent Study, Directed Study (one-to-one)
This introductory course will address basic and advanced 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.
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
This course covers systematic reviews of the literature for controlled clinical trials and observational studies. Statistical methods and computer software is 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 trials, investigating variability between studies, systematic reviews of observational studies, statistical methods and computer software, using systematic reviews in practice, the Cochrane collaboration, and other evidence-based medicine topics.
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.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8110, STAT8130
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
This course consists of clinical and translational research seminars by MCG faculty members and visiting researchers. Students will have an opportunity to talk to each speaker informally and to serve as hosts to visiting scientists.
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.
Grade Mode: Satisfactory, Audit
Credit Hours: 1 to 12
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
Prerequisites: All other biostatistics courses except Time-To-Event Data Analysis - STAT 8320.
This course serves as an introduction to Generalized Linear Models (GLMs). It instructs students in a unifying theory that combines the areas of linear models, non-linear models, regression, categorical data, and analysis of variance.
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
Introduction to statistical methods in the analysis of DNA and protein sequence data. This course exposes students to applications of statistical theory to assembling biological sequences, making inferences about single sequences, and comparing two or more sequences. Statistical foundations of BLAST tests are covered.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8120, STAT8220, STAT8321 Pre-Req Min Grade: C, C, C
Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
The statistical analysis of complex phenotypes. Topics include genotypic value, genetic variance, and linear models. Environmental variance, genotype by environment interaction, threshold models and generalized linear mixed models, mapping quantitative trait loci (QTL), and variance component estimation.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8331 Pre-Req Min Grade: C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
This course provides a hands-on exposure to programming, data management and report generation with one of the most popular statistical software packages.
Prerequisite: College algebra
This course covers systematic reviews of the literature for controlled clinical trials and observational studies. Statistical methods and computer software is 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 trials, investigating variability between studies, systematic reviews of observational studies, statistical methods and computer software, using systematic reviews in practice, the Cochrane collaboration, and other evidence-based medicine topics.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8341 Pre-Req Min Grade: C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
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.
Prerequisites: Multivariable Calculus and Matrix Algebra.
Introduction to modeling and analyzing expression data of microarrays. Methods of cluster analysis will be covered as ways to attempt to group genes of the same biochemical pathways together. Students will also learn to test hypotheses related to microarray designs, with emphasis on determining which genes are differentially expressed between two populations.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8120, STAT8220, STAT8321 Pre-Req Min Grade: C, C, C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
Advanced statistical analyses specific for medical and health data and designs involving humans. Topics included are linkage analyses, association studies, linkage disequilibrium mapping, segregation analyses, and gene and environment interaction.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8432 Pre-Req Min Grade: C
College Restrictions: Graduate Studies Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
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.
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. Course Prerequisites: All core biostatistics courses (except STAT8320) and one of the three elective module courses.
Grade Mode: Satisfactory, Audit
Credit Hours: 3 to 11 Contact Hours: 0 Lecture Hours: 0 Lab Hours: 0 Other Hours: 0
Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Doctor of Philosophy, Master of Science
Schedule Type: Lecture, Directed Study (one-to-one)
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.
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.
This course will cover simple linear regresssion 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.
Finite probability models, Markov chains, martingales, random walk, Possion processes, model elements of renewal and reliability theory, Brownian motion, stochastic differential equations.
This course offers an introduction to the analysis of observed times to events, e.g., times to death (survival times). The course focuses on methods of regression generalized to the case of censored survival data. Regression models studied include non-parametric (Kaplan-Meier), semi-parametric (Cox’s PH Model), and parametric regression models (Exponential, Weibull, Log-Logistic, & others). Other topics covered include model development, model adequacy, extensions to the Cox PH model, recurrent event models and frailty models.
In addition to providing a basic introduction to genetics, this course also aims to connect fundamental principles of biology and 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.
This course is designed for student to gain practical experience in integration of statistical theory and application in current research, systematic formulation of research problems, data formatting, data collection, study design, data analysis, and interpretation and communication of results.
This course is designed to cover special topics in 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.
Prerequisites: Permission of 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.
Grade Mode: Satisfactory, Audit
Credit Hours: 1 to 12 Contact Hours: 1 to 12 Lab Hours: 1 to 12
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
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).
Grade Mode: Satisfactory, Audit
Credit Hours: 1 to 9 Contact Hours: 1 to 9 Lab Hours: 1 to 9
Prerequisite: Consent of Major Advisor.
The thesis project for the MS program will be for 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.
The thesis project for the MS program will be of two types: 1) Use of established but state-of -the-art statistical tools to analyze and report on collected data sets or 2) A rigorous review of statistical literature, possibly involving a small amount of methodological research, that has potential use in complex biomedical data analysis. Course Prerequisites: All core biostatistics courses (except STAT8320) and one of the three elective module courses.
Grade Mode: Satisfactory
Credit Hours: 3 to 11 Contact Hours: 0 Lecture Hours: 0 Lab Hours: 0 Other Hours: 0
College Restrictions: Graduate Studies Program Restrictions: MS_BIOS-Biostatistics Campus Restrictions: Main campus Level Restrictions: Graduate Semester Class Restrictions: Graduate Degree Restrictions: Master of Science
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 that full rank. Both balanced and unbalanced random and mixed models will also be covered.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8620, STAT8630
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
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.
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 multi-factorial 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.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8640
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
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.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8630
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
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.
Course Prerequisites: STAT 8610 and STAT 8210 and STAT 8620 or STAT 8220 or approval from course director.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8620, STAT8630
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
Prerequisites: STAT 8620
Families of models, likelihood, sufficiency, significance tests, composite null and alternative hypotheses, similar regions, invariant test, interval estimation, point estimation, bias and variance, Cramer-Rao inequality, asymptotic theory, large-sample inference, likelihood ratio test, score test, Wald’s test.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8620
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
Prerequisites: STAT 8620 and STAT 9170
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.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8620, STAT9170
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
This course introduces Bayesian methods for statistical inferences. We will look over concepts of Bayesian theories and Bayesian coomputational 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.
Rigorous statistical and computational treatment of methods for localizing genes and environmental effects involved in the etiology of complex human traits using case-control 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.
Course Prerequisites: STAT 9150 and STAT 9170
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT9150, STAT9170
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
Computational inference and visualization approaches for high-thoughput data from genomics and proteomics. Topics include an introduction to high-thoughput experimental data, experiment planning, data normalization, data representation, clustering, classification, approaches for detecting differential expression, hierarchical Bayesian models, Gayesian variable selection, other computational approaches to variable selection, statistical network models, and statistical metrics for model validation.
Grade Mode: Normal, Audit
Prerequisites: Prerequisites: STAT8640, STAT9170
Credit Hours: 3 Contact Hours: 3 Lecture Hours: 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
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.
Grade Mode: Normal, Audit
Credit Hours: 1 to 3 Contact Hours: 1 to 3 Lecture Hours: 1 to 3
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester
Prerequisites: 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.
Grade Mode: Satisfactory, Audit
Credit Hours: 1 to 12
College Restrictions: Graduate Studies Level Restrictions: Graduate Semester