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Course Criteria
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3.00 Credits
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. 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department Course Attributes: Elective Course
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3.00 Credits
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. 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
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. Course prerequisites: STAT 8620 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department
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3.00 Credits
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. Course Prerequisites: STAT 8620 and STAT 9170 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department Course Attributes: Elective Course
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3.00 Credits
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 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department Course Attributes: Elective Course
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3.00 Credits
Computational inference and visualization approaches for high-thoughput data from genomics and proteomics. Topics include and introduction to high-thoughput experimental data, experiment planning, data normalization, data representation, clustering, classification, approaches for detecting differential experession, hierarchical Bayesian models, Gayesian viariable selection, other computational approaches to varialbe selection, statistical network models, and statistical metrics for model validation. 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department Course Attributes: Elective Course
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1.00 - 3.00 Credits
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 intresets of the instructor and the students. 1.000 TO 3.000 Credit Hours 1.000 TO 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department Course Attributes: Elective Course
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3.00 Credits
The student works closely with his/her faculty mentor on an in-depth study of a research question of intrest to both student and advisor. The course may be repeated as necessary until the student completes the research. Course Prerequisites: Admission to PH.D. candidacy and permission of Major Advisor. 3.000 TO 12.000 Credit Hours Levels: Graduate Semester Schedule Types: Dissertation Graduate Studies College Biostatistics Department
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3.00 Credits
Prerequisite: Successful completion of Phase II This four (4) week clerkship provides fundamental experience in general surgery. Although most of the students' time will be spent helping to care for inpatients, they will also participate in outpatient clinics. Emphasis will be placed on diagnostic evaluation, as well as preoperative and postoperative care. Evaluation of common outpatient conditions often seen by surgeons will also be emphasized. 10.000 TO 20.000 Credit Hours 1.000 Lecture hours 30.000 Other hours Levels: Professional Semester Schedule Types: Lecture/Supervised Lab/Clinic Medicine College SOM Surgery Department
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3.00 Credits
Prerequisites: SUR 5000A Responsibilities of initial evaluations, pre and postoperative planning and care are under the supervision of the surgical resident house staff and the Director of Surgical education. Experience will include the care of peripheral vascular, thoracic, colorectal and general surgical patients as well as a broad exposure to all other surgical specialties. 10.000 Credit Hours 15.000 Lecture hours 40.000 Other hours Levels: Professional Semester Schedule Types: Internship/Practicum Medicine College SOM Surgery Department
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