- Introduction to Clinical EpidemiologyFall term (first half of term)
This course is a series of lectures introducing basic principles of epidemiologic research design. Lectures include the following topics: definitions of epidemiology; measures of disease frequency; measures of effect and association; epidemiologic study designs, both experimental and non-experimental; data collection methods; and an overview of analysis of epidemiologic studies.
- Clinical TrialsSpring term (first half of term)
This course will cover methods in the design, conduct, and reporting of clinical trials. Topics to be covered include: study design and biostatistical considerations specific to drug development (with particular emphasis on cancer research); research ethics; regulatory and monitoring issues; data management; and methods for the incorporation of biological endpoints into clinical trial design/implementation. A primer on publishing results is also provided.
- Database ManagementSpring term (second half of term)
This course provides students with an introduction to the techniques of data collection and database management as they apply to clinical research. Students learn how to design and implement computerized databases and electronic surveys using the REDCap platform, perform basic data import and export operations, protect the security and confidentiality of study data, and perform quality assurance procedures. This course focuses on the practical issues of database management.
- Biostatistics in PracticeFall term (second half of term)
This course is designed for collaborative researchers and will provide an overview of the fundamental concepts of biostatistics. Topics covered include descriptive statistics, sampling distribution, confidence intervals, hypothesis testing, t-test, analysis of variance, linear model, logistic regression, and survival data analysis. The course includes a laboratory session demonstrating the use of STATA to carry out tests discussed in the lecture. Emphasis in this course is placed on understanding data properties, choosing appropriate analysis methods, performing analysis, and interpreting study results.