Statistical Methods for the Design and Analysis of Studies with Incomplete Data

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Missing data is a common problem that can be encountered in any research setting due to unanticipated problems in the implementation of a study. Conclusions drawn from such a study are only valid if the missing data are appropriately handled in the statistical analyses. However, statistical methodology is underdeveloped in settings where there are multiple mechanisms contributing to missingness. If interest lies in a composite score that is constructed from several potentially missing variables, then the missing data mechanism will be complex and model misspecification becomes a real concern. I will develop statistical methodologies and provide practical recommendations for effective analysis strategies in these settings.