Publication Date
Fall 9-13-2024
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 missing 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 imputation by Heckman's One-Step ML Estimation for binary MNAR outcome and continuous 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.
Degree Name
Statistics
Level of Degree
Doctoral
Department Name
Mathematics & Statistics
First Committee Member (Chair)
James Degnan
Second Committee Member
Orrin Myers
Third Committee Member
Akshay Sood
Fourth Committee Member
Fletcher Christensen
Language
English
Keywords
Missing data, Multiple Imputation, Missing Not at Random
Document Type
Dissertation
Recommended Citation
Shore, Xin W.. "Improvement and Evaluation of Multiple Imputation by Heckman's One-Step MLE for Binary MNAR Outcomes and Various Types of MAR Covariates." (2024). https://digitalrepository.unm.edu/math_etds/229
