Missing data are unavoidable in many studies, especially those that collect information on humans. Failure to address missing data may result in misleading conclusions. A dataset may contain missing values for a variety of reasons. For example, survey respondents may refuse to answer questions of a sensitive nature or patients participating in longitudinal studies may drop out before its conclusion. Center faculty have been at the forefront of developing statistical methods to handle missing data. Specifically, Prof. Hogan has done significant work on missing data in longitudinal studies and sensitivity analysis. Prof. Gutman has developed various imputation methods for application in health services research.