Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings.

A F Donneau, M Mauer, P Lambert, G Molenberghs, A Albert
Author Information
  1. A F Donneau: a Medical Informatics and Biostatistics, Department of Public Health , University of Liège , Liège , Belgium.

Abstract

The application of multiple imputation (MI) techniques as a preliminary step to handle missing values in data analysis is well established. The MI method can be classified into two broad classes, the joint modeling and the fully conditional specification approaches. Their relative performance for the longitudinal ordinal data setting under the missing at random (MAR) assumption is not well documented. This article intends to fill this gap by conducting a large simulation study on the estimation of the parameters of a longitudinal proportional odds model. The two MI methods are also illustrated in quality of life data from a cancer clinical trial.

Keywords

MeSH Term

Central Nervous System Neoplasms
Computer Simulation
Glioblastoma
Humans
Logistic Models
Longitudinal Studies
Models, Statistical
Multivariate Analysis
Patient Dropouts
Quality of Life
Randomized Controlled Trials as Topic
Statistical Distributions
Surveys and Questionnaires

Word Cloud

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