Course Description:

This course focuses on practical application of statistics addressing clinical research questions. Analyzing data is the major emphasis of the course including examining if assumptions of the statistical analyses are being met and interpreting the findings. Course assignments and a final project focus on using statistical software and computing resources to analyze data sets from actual clinical research studies and literature with interpretation and assessment of conclusions.

Prerequisites: BIOS 500 and 501

Course Objective:

To build and expand upon the statistical theory and methods learned in BIOS 500 and 501 and improve the student’s statistical software experience and programming skills (course includes analysis using SAS, SPSS or R) to improve research scholarship and dissemination.

Planned Topics:

• Computing Environment (SAS, SPSS, R, Other Supporting Software)
• Getting data into and out of statistical software (import, export features)
• Reproducible Research Principles (documentation, reporting, version control)
• Initial data assessments: univariate and bivariate methods, parametric and non-parametric
• Regression methods: linear, logistic and introduction to “generalized”
• Analysis of (co)Variance: univariate and multivariate, ANOVA, ANCOVA, MANOVA, MANCOVA
• Longitudinal analysis: repeated measures with introduction to multi-level models (MLM)
• Assessment and testing of data & model assumptions (including missing data)
• Brief introduction to Factor Analysis, Reliability, Discriminant Analysis, and SEM (structural equation modeling)