Xin (Karen) Shore
MS Thesis defense, Statistics

Title
Improvement and Evaluation of Multiple Imputation by Heckman’s One-Step ML Estimation for Binary MNAR Outcomes and Various Types of MAR Covariates

Abstract
Missing data is inevitable in clinical epidemiology. It becomes one of the major
challenges in the analyses and can potentially undermine the validity of results and
conclusions. Although methods for handling missing data with mechanisms of miss-
ing completely at random (MCAR) or missing at random (MAR) have been widely
researched, methods adapted for the missing not at random (MNAR) mechanism are
less studied. Galimard et al. (2018) have derived a method to use multiple impu-
tation by Heckman’s One-Step ML Estimation for binary MNAR outcome and con-
tinuous MAR covariates (MIHEml ). This dissertation focuses on updating MIHEml
in terms of methodology and programming. Both the simulation and the following
empirical analysis prove that a more upgraded and generalized MIHEml algorithm
manages to improve the estimation accuracy and prediction accuracy, comparing
with complete case analysis to handle a binary MNAR outcome and various types
of MAR covariates in the same process. Thus, this study reassures the effectiveness
and applicability of the updated MIHEml.