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Course Criteria
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3.00 Credits
This course emphasizes data management and software applications using the SAS (Statistical Analysis System) software package. It will introduce the student to SAS codes for: inputting and outputting data, creating temporary and permanent data sets, creating formatted and labeled SAS data sets, merging and connecting SAS data sets, creating output using the TABULATE and REPORT procedures, debugging a SAS program that includes the TABULATE, REPORT and SQL procedures, using character functions in SAS, using a random number generator, probability distributions, arrays, and date and time functions. Students will also write a simple and complex query using the SQL procedure; create, populate and modify a set of tables/views using the SQL procedure; and create a SAS program which includes one or more macros. This course will cover basic relational database design and descriptive statistics in SAS. Particular focus is placed on applications pertaining to public health and biomedical research.
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3.00 Credits
The class is designed to provide skill building and practical experience in using SAS to: create analysis data files; analyze data such as that found in typical biostatistical consulting problems; and assess the validity of analysis methodology assumptions.
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1.00 - 3.00 Credits
Provides the student with an opportunity to investigate an area of interest under the direction of a faculty mentor.
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3.00 Credits
This course is designed to provide the student with the current best practices in biostatistical consulting. Topics include: Identifying and constructing appropriate questions to ask clients regarding their consultation request, an overview of appropriate statistical methods and SAS software procedures to use for specific study designs and statistical analysis of collected data, directing a consultation appointment without faculty lead, conducting exploratory data analyses, conducting effective analyses based on appropriate statistical methods and providing oral and written communication of statistical findings.
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3.00 Credits
This course provides an advanced study of theoretical statistics. Topics include: an introduction probability and sample space, conditional probability and Bayes Theorem, probability distribution of a random variable, discrete and continuous random variables, functions of random variables, moments and moment generating functions, order statistics and their distributions, discrete distributions, continuous distributions, bivariate and multivariate normal distribution, modes of convergence, limiting moment generating functions, and the central limit theorems.
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3.00 Credits
This course is a continuation of Advanced Statistical Inference for Biostatisticians I. The additional topics in this course consists of: sample moments and their distributions, the theory of point estimation, the Neyman-Pearson Theory of testing hypotheses, likelihood ratio test, chi-square tests, t-test, F-test, Bayes and Minimax procedures in hypothesis testing, confidence estimation, the general linear hypothesis, and nonparametric statistical inference.
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3.00 Credits
This course provides the student with an introduction Bayesian Analysis and compares Bayesian methods to that of frequentists. The course includes selection of prior distributions, computing posterior distributions, conjugate models, Beta-Binomial model, Normal-Normal model, and Gamma-Poisson model. Bayesian inference using point and interval estimation, Bayesian hierarchical models, and exchangeability will be explored. Topics including Empirical Bayes versus a fully Bayes approach, Markov Chain Monte Carlo methods and model checking using Bayes factors and sensitivity analyses will be included.
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3.00 Credits
This course is a continuation of Bayesian Statistics I. In this course the student will study data collection and design of Bayesian analyses, including ignorability issues, Normal linear regression under the Bayesian paradigm, Markov Chain Monte Carlo methods, including the Gibbs sampler and Metropolis-Hastings algorithms, model checking and sensitivity analysis for model robustness, and Bayesian generalized linear models.
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3.00 Credits
This course is designed to address research questions in biomedical and other health-related research using meta-analysis techniques. A survey of past and present challenges of such techniques will be addressed, as will a mixture of Frequentist and Bayesian approaches to meta-analysis. Typical research questions found in health-related issues such as prevention, diagnosis, treatment, and policy will be constructed, followed by the methodologies to analyze such health-related questions. The course will focus on modeling and implementation issues in meta-analysis for biostatistical applications. In particular, this course will emphasize such topics as heterogeneous study results, combining studies with different designs, advantages and disadvantages to using meta-analysis over large trials, meta-analysis for 2x2 tables with multiple treatment groups, meta-analysis of clinical trials, addressing biases, meta-analysis of patient survival data, among additional biomedical applications.
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3.00 Credits
This course provides an introduction to longitudinal and clustered data. Topics include the basic concepts of longitudinal data, linear models for longitudinal data, generalized linear models and salient features, generalized estimating equations, generalized linear mixed effects models, missing data and dropouts, sample size and power, repeated measures, and multilevel linear models.
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