Although missing data are pervasive in studies across disciplines, the impact of missing data on estimation and inference and the strengths and weaknesses of modern approaches to handling missing data are not widely understood.

This module will review common missing data mechanisms, then introduce a variety of methods for estimation and inference in the presence of missing data, including conventional methods, the EM algorithm, multiple imputation, and semi-parametric methods. Approaches to sensitivity analyses will also be discussed. All methods will be illustrated in R using data from observational studies.

This course is targeted towards individuals with little or no prior experience with modern missing data methods. Experience using regression methods to analyze data (e.g., linear regression, logistic regression) is important background for this module.