Date of Degree
Access restricted until 07/03/2020
PhD (Doctor of Philosophy)
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Fifth Committee Member
Imputation is one well recognized method for handling missing data. Multiple imputation provides a framework for imputing missing data that incorporate uncertainty about the imputations at the analysis stage. An important factor to consider when performing multiple imputation is the imputation model. In particular, a careful choice of the covariates to include in the model is crucial. The current recommendation by several authors in the literature (Van Buren, 2012; Moons et al., 2006, Little and Rubin, 2002) is to include all variables that will appear in the analytical model including the outcome as covariates in the imputation model. When the goal of the analysis is to explore the relationship between the outcome and the variable with missing data (the target variable), this recommendation seems questionable. Should we make use of the outcome to fill-in the target variable missing observations and then use these filled-in observations along with the observed data on the target variable to explore the relationship of the target variable with the outcome? We believe that this approach is circular. Instead, we have designed multiple imputation approaches rooted in machines learning techniques that avoid the use of the outcome at the imputation stage and maintain reasonable inferential properties. We also compare our approaches performances to currently available methods.
clustering, imputation model, multiple imputation, penalized splines
xiii, 277 pages
Includes bibliographical references (pages 275-277).
Copyright © 2018 Monelle Tamegnon
Tamegnon, Monelle. "Avoiding the redundant effect on regression analyses of including an outcome in the imputation model." PhD (Doctor of Philosophy) thesis, University of Iowa, 2018.