Topics covered during the Institute include:
- Basic probability and statistical inference
- Regression analysis, including linear and logistic regression, with applications such as survival analysis and risk predication
- Research ethics and human subjects protections
- Functional data analysis
- Causal inference
- Time-to-event data and staggered study entry
- Machine learning
- Clinical trials
- Personalized medicine
- Use of electronic health records and patient reported data in research
On most days (Mon-Fri), instruction will be delivered in multiple formats, including lectures, special presentations, group panel discussions, and “hands-on” activities where students learn to use statistical software for data analyses and computer simulation exercises. There will typically be two instruction sessions each day: one in the morning from 10am-12pm ET and one in the afternoon from 1:30-3:30 ET.
Each week will feature a field trip related to issues of importance to biostatistics.
Following common statistical practice, students will be randomized into teams of 3-4. During the last week of the program, these teams will apply the techniques learned in class to analyze the data in a hack-a-thon style (friendly) competition. Teams will present their results during the last Thursday of the program. To allow students to get to know each other, teams will be rotated a few times during the program.
Students who participate in SIBS will receive 4 credit hours for the course:
ST 401 Experiences in Data Analysis
Course description: This course will allow students to see many practical aspects of data analysis and machine learning methods. Each section of this course will expose students to the process of data analysis in a themed area such as biostatistics or environmental statistics. Students will see problems of data collection and analysis through a combination of classroom demonstrations, hands on computer activities and visits to local industries.
Biostatistics theme: Introduction to biostatistical methods and the role of statistics in research in medicine and public health. Fundamentals of
probability and statistical inference, including estimation, hypothesis testing and statistical regression. Randomization and design of clinical
trials; statistics in drug development; linear and logistic regression analysis; statistical methods for time-to-event data; early stopping of
clinical trials; methods for handling missing data; methods for analysis of observational studies and adjustment for confounding; human subjects
protections and research ethics; careers in biostatistics.