Date of Award

1-1-2017

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Epidemiology and Biostatistics

Program

Biostatistics

Content Description

1 online resource (iii, ix, 61 pages) : illustrations (some color)

Dissertation/Thesis Chair

Recai M Yucel

Committee Members

Daniel F Martinez, Tabassum Insaf

Keywords

Calibration-based rounding in Ordinal Clustered Data, Multiple Imputation, Multiple Imputation in Clustered Data, Multiple Imputation in Multiple Membership Multilevel Data, Multiple Membership Multilevel Data, Sequential Multiple Imputation in Clustered Data, Missing observations (Statistics), Multiple imputation (Statistics), Monte Carlo method

Subject Categories

Biostatistics

Abstract

Presence of missing data in correlated data settings is a non-trivial problem. Inference by multiple imputation offers a viable solution to analysts. However, the missing data problem is typically more complicated due to diverse measurement scales, skip patterns, bounds and restrictions. Sequential regression imputation also known as variable-by-variable imputation has emerged as a popular imputation modeling technique, especially in the complex data structures. In this dissertation, we develop three methods to handle incomplete data in hierarchically nested and non-nested multilevel data structures using sequential regression imputation approach.

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