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Course Criteria
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3.00 Credits
This course provides an introduction to the fundamental knowledge of derivatives and integrals found in biostatistical inference. The course will introduce the theory of probability, expectation and variance of discrete and continuous distributions, moment generating functions, bivariate and multivariate distributions, maximum likelihood estimation, and bias. Emphasis will be placed on the development of critical thinking skills and how concepts in this course are used in public health and biomedical studies.
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3.00 Credits
This course introduces the methods for analyzing biomedical and health related data using linear regression models. The course will introduce the student to matrix algebra as used in linear models. The course will involve model selection, diagnosis and remedial techniques to correct for assumption violations. The students will learn how to apply SAS procedures PROC REG, PROC CORR, and PROC GLM and interpret the results of analysis. Emphasis will also be placed on the development of critical thinking skills.
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3.00 Credits
This course introduces the student to experimental designs commonly used in public health and biomedical settings and the methods for analyzing them. It will introduce the student to the principles of designing an experiment (randomization, blocking and replication), completely randomized designs, factorial design, randomized block designs, nested designs, split-plot designs, crossover designs, Latin squares and analysis of the longitudinal designs, a fixed effect (Model I) single factor and multifactor experiment, a random effect (Model II) single factor and multifactor experiment, a mixed effect (Model III) multifactor experiment, and covariance model . Students will learn how to apply SAS procedures: PROC GLM, PROC MIXED, PROC GENMOD, PROC VARCOMP, PROC RSREG and PROC MULTTEST to public health and biomedical data and interpret the results of the analysis.
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3.00 Credits
This course introduces statistical methods for analyzing both univariate and multivariate categorical and count data in public health, biomedical research, and other health-related fields. The course will introduce how to distinguish among the different measurement scales in addition to the commonly used statistical probability distribution and inference methods for categorical and count data. Emphasis will be placed on the application of the methodology and computational aspects rather than theory. The students will learn how to apply SAS procedures to data and interpret the results.
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1.00 - 3.00 Credits
Allows the student the opportunity to receive specialized and/or focused instruction in a biostatistical topic not generally offered by the department.
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3.00 Credits
This course introduces statistical methods for analyzing data collected on the time to an event, referred to as survival data, in medical research and other health related fields. Emphasis will be placed on the application of the methodology and computational aspects rather than theory. The students will learn how to apply SAS procedures to data and interpret the results.
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3.00 Credits
Students are introduced to regulatory, scientific, statistical and practical aspects of methods inherent in design, monitoring and analyzing clinical trials. Clinical trials in many areas of drug development are presented, discussed and critiqued.
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3.00 Credits
This course is designed to provide students in biostatistics with an introduction to multivariate methods commonly found in health related fields. The course will emphasize multivariate regression, multivariate analysis of variance (MANOVA) and co-variance (MANCOVA), discriminant analysis and an alternative to logistic regression and cluster analysis. Students will be introduced to appropriate SAS procedures and be required to interpret and report their results in a form that meets both FDA and the International Committee on Harmonization.
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3.00 Credits
Major statistical issues in the federal regulation of drug research and clinical development will be studied. Specifically, summarization, analysis and monitoring of adverse experiences, two treatment crossover designs, active control equivalence studies, optimization in clinical trials and combination drug development, dosing in the elderly, intention to treat in clinical trials, and dual control groups in rodent carcinogenicity studies will be studied.
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3.00 Credits
Statistical aspects of drug research and development, federal regulations, and marketing will be studied. Specifically, statistical methods useful in the areas of pharmaceutical discovery and optimization, assessment of pharmaceutical activity, pharmaceutical formulation, preclinical and clinical safety assessment, clinical development, bio-availability and bio-equivalence, clinical traits with quantitative and qualitative measurements, cancer clinical trials, and manufacturing and quality control processes will be studied.
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