Biostatistics Certificate 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 designed to prepare students to communicate effectively in English within the pharmaceutical industry. This course will equip learners with the linguistic skills and special vocabulary required to understand daily situations in the target work environment. A variety of engaging topics and motivating role-plays, as well as extensive practical exercises, provide our students with an understanding of how to communicate effectively in different areas of the pharmaceutical industry.

This course provides foundations of database systems, focusing on basics such as relational algebra and data modeling, schema normalization, query optimization and transactions. It teaches the algebraic query language that forms the formal foundations of SQL as well as SQL advanced features. It is designed for students who have no prior database experience, although students who have taken an undergraduate course in databases are encouraged to attend.

This course explores the collection, analysis and interpretation of data. It develops an understanding of the appropriateness of different methods of data collection, with a particular focus on the methods used to sample data from a population. Students will be taught to recognize and provide examples of the different types of data that arise in public health and clinical studies, interpret differences in data distributions via visual displays, calculate standard normal scores and resulting probabilities and calculate and interpret confidence intervals for population means and proportions. Students will be exposed to a theoretical framework for linear and generalized models. The course covers the following linear models: multivariate normal theory, least squares estimation, limiting chi-square and F-distributions, sum of squares (partial, sequential) and expected sum of squares, weighted least squares, orthogonality, analysis of variance (ANOVA). The second half of the semester focuses on generalized linear models: binomial, Poisson, multinomial errors, introduction to categorical data analysis, conditional likelihoods, quasi-likelihoods, model checking and matched pair designs.

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.

This course is designed to prepare students to communicate effectively in English within the pharmaceutical industry. This course will equip learners with the linguistic skills and special vocabulary required to understand daily situations in a work environment. A variety of engaging topics and motivating role-plays, as well as an extensive number of practical exercises, will provide students with an understanding of how to communicate effectively in the different areas of the pharmaceutical industry.

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 prepare students for taking the SAS® Base Programming for SAS® 9 exam. It provides full information about SAS® certification, advantages of certification, typical challenges students will encounter during the exam, an overview of technical aspects and practical experience of mock tests.