Course info

Course syllabus

Class notes

Homework assignments and solutions





Course objective:

Missing data are ubiquitous in almost every area of scientific inquiry, especially in health sciences research involving human subjects, and have important implications for data analysis. At the very least, there is a loss of information and reduction in precision of inference on the population of interest relative to that intended. Of greater concern is the potential for biased inference that can result if the reasons for missingness are related to outcomes of interest. Accordingly, principled methods to take this challenge into appropriate account are required. This course will provide an overview of modern statistical frameworks and methods for analysis in the presence of missing data. Both methodological developments and applications will be emphasized.

Course prerequisites

ST 522, Statistical Theory II, and ST 552, Linear Models and Variance Components, or equivalents. Students should also have been exposed to SAS and R and have reasonable proramming skills. Please see the instructors if you have questions about the suitability of your background.

Course topics

See the class notes for more detailed information