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EPID 620: Statisical Inference I
3.00 Credits
University of Pennsylvania
Faculty. Prerequisite(s): BSTA 620. Statistical inference including estimation, confidence intervals, hypothesis tests and non-parametric methods.
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EPID 620 - Statisical Inference I
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EPID 621: Statistical Inference II
3.00 Credits
University of Pennsylvania
Brown. Prerequisite(s): BSTA 621. Statistical inference including estimation, confidence intervals, hypothesis tests and non-parametric methods.
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EPID 621 - Statistical Inference II
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EPID 623: Applied Survival Analysis
3.00 Credits
University of Pennsylvania
Applied Survival Analysis
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EPID 623 - Applied Survival Analysis
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EPID 630: Statistical Methods for Data Analysis I
3.00 Credits
University of Pennsylvania
Shults and Putt. Prerequisite(s): Multivariable calculus and linear algebra, BSTA 620 (may be taken concurrently). This first course in statistical methods for data analysis is aimed at first year Biostatistics degree candidates. It focuses on the analysis of continuous data, and includes descriptive statistics, such as central tendencies, dispersion measures, shapes of a distribution, graphical representations of distributions, transformations, and testing for goodness of fit for a distribution. Populations, samples, hypotheses of differences and equivalence, and errors will be defined. One and two sample t-tests, analysis of variance, correlation, as well as non-parametric tests and correlations will be covered. Estimation, including confidence intervals, and robust methods will be discussed. The relationship between outcome variables and explanatory variables will be examined via regression analysis, including single linear regression, multiple regression, model fitting and testing, partial correlation, residuals, multicolinearity. Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.
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EPID 630 - Statistical Methods for Data Analysis I
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EPID 631: Statistical Methods and Data Analysis II
3.00 Credits
University of Pennsylvania
Gimotty. Prerequisite(s): linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently). This is the second half of the methods sequence and focuses on categorical data and survival data. Topics in categorical data to be covered include defining rates, incidence and prevalence, the chi-squared test, Fisher's exact test and its extension, relative risk and odds-ratio, sensitivity, specificity, predictive values, logistic regression with goodness of fit tests, ROC curves, Mantel-Haenszel test, McNemar's test, the Poisson model, and the Kappa statistic. Survival analysis will include defining the survival curve, censoring, and the hazard function, the Kaplan-Meier estimate, Greenwood's formula and confidence bands, the log rank test, and Cox's proportional hazards regression models. Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.
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EPID 631 - Statistical Methods and Data Analysis II
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EPID 633: Advanced Database Mangement for Clinical Research
3.00 Credits
University of Pennsylvania
Holmes. This course is intended to provide in-depth, practical exposure to the design, implementation, and use of secondary data resoources in clinical research. This course is inteneded to provide students with the skills needed to design and conduct a research project using secondary data, with a focuson data management. We will focus on analysis only to th extent that one needs to be aware of the demands that particualar analytic strategies put on the structure and management of data.
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EPID 633 - Advanced Database Mangement for Clinical Research
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EPID 651: Introduction to Linear Models and Generalized Linear Models
3.00 Credits
University of Pennsylvania
Tu. Prerequisite(s): linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently). This course extends the content on linear models in BSTA 630 and BSTA 631 to more advanced concepts and applications of linear models. Topics include the matrix approach to linear models including regression and analysis of variance, general linear hypothesis, estimability, polynomial, piecewise, ridge, and weighted regression, regression and collinearity diagnostics, multiple comparisons, fitting strategies, simple experimental designs (block designs, split plot), random effects models, Best Linear Unbiased Prediction. In addition, generalized linear models will be introduced with emphasis on the binomial, logit and Poisson log-linear models. Applications of methods to example data sets will be emphasized.
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EPID 651 - Introduction to Linear Models and Generalized Linear Models
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EPID 656: Research Methods in ID Epidemiology
3.00 Credits
University of Pennsylvania
Research Methods in ID Epidemiology
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EPID 656 - Research Methods in ID Epidemiology
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EPID 658: Gastroenterology EPI
3.00 Credits
University of Pennsylvania
Gastroenterology EPI
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EPID 658 - Gastroenterology EPI
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EPID 690: Ethical Issues In Clinical Research
3.00 Credits
University of Pennsylvania
Ethical Issues In Clinical Research
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EPID 690 - Ethical Issues In Clinical Research
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