UCF Clinical SAS® Internship Program

This course is designed to teach students how to execute SAS® programs in a Microsoft Windows environment. It provides an overview of the SAS® system under Microsoft Windows and a fundamental grounding in the following: SAS® DATA step, basic data manipulation including reading raw data into SAS®, using formats and informants, functions, conditional processing, subsetting and joining data sets. The course is also designed to cover the basic reporting procedures, including PROC Print, PROC Freq, PROC Means, PROC Tabulate and PROC Report, as well as the output delivery system (ODS), SQL, SAS®/GRAPH, macros, arrays and other Base SAS® procedures. This course provides a solid grounding in the more advanced features of SAS®.

This course acts as an introduction to the fundamental concepts and approaches that govern the design, analysis and interpretation of clinical research studies. It provides a basic overview of the clinical research industry and new drug development. Students will be introduced to the language that is typically associated with clinical research and the pharmaceutical industry. They will have an opportunity to learn, understand and apply this terminology in the context of clinical research. This course also covers an overview of the process of new drug development from discovery through regulatory to approval and introduction to the market. It’s designed to provide students with the background needed to pursue a number of additional courses in the areas of biostatistics, clinical data analysis and biostatistical programming.

This course is highly relevant for modeling and data analysis in many areas, including medicine, actuarial science, economics and other social sciences. Statistics II covers classic repeated measures model, random effect models, generalized estimating equations (GEEs), hierarchical models and transitional models for binary data, marginal vs. mixed effects models, model fitting, model checking, clustering and implications for study design. The module also includes discussion of missing data techniques, Bayesian and likelihood methods for GLMs and various fitting algorithms such as maximum likelihood and generalized least squares. This course also provides an introduction to methods for time-to-event data with censoring mechanisms. Topics include life tables, nonparametric approaches (e.g., Kaplan-Meir, log-rank), semi-parametric approaches (e.g., Cox model), competing risks (introduction to Poisson regression as connection to the Cox model) and time-dependent covariates. Additionally, the methodology and rationale for Bayesian methods and their applications will be briefly discussed.

This course is designed to be very hands-on and focused on the practical aspects of performing clinical trial analyses in the pharmaceutical industry. Skills will include creating datasets, tables and listings, preparing graphics, and performing commonly used statistical analysis. Important topics such as CDISC, MedDRA, WHODrug and SOPs, and other industry regulations and standards, will also be presented to the students during the course. During the workshops, students will import and export raw data files, manipulate and transform data, combine SAS® data sets, perform analysis, create basic detail and summary reports using SAS® procedures, identify and correct data, syntax and programming logic errors and use output delivery systems. Health-related data sets will be provided for students to use.

The data interpretation module is designed to help students who have no background in life sciences prepare for data interpretation assessments in clinical practice. It explores a number of key topics in medicine. Each topic is set around an image or investigation, such as vital signs, an X-ray, CT scan or blood film, that is used to test identification and interpretation of the data provided. Through explanation of the correct and incorrect answers, students can learn from their mistakes in a safe setting. This course aims to teach students how to interpret clinical data and the outputs generated during the Clinical SAS® Programming module.