A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data

被引：8

作者：

Donneau, A. F. ^{[1
]}

Mauer, M. ^{[2
]}

Molenberghs, G. ^{[3
,4
]}

Albert, A. ^{[1
]}

机构：

[1] Univ Liege, B-4000 Liege, Belgium

[2] EORTC Headquarters, Dept Stat & Qual Life, Brussels, Belgium

[3] Univ Hasselt, I BioStat, Diepenbeek, Belgium

[4] Katholieke Univ Leuven, I BioStat, Leuven, Belgium

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2015年 / 44卷 / 05期

关键词：

Longitudinal analysis; Missing at random; Multiple imputation; Ordinal variables; 62H12; 62J12; 65C10; 62-04; 92B15; REGRESSION-MODELS;

D O I：

10.1080/03610918.2013.818690

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Multiple imputation (MI) is now a reference solution for handling missing data. The default method for MI is the Multivariate Normal Imputation (MNI) algorithm that is based on the multivariate normal distribution. In the presence of longitudinal ordinal missing data, where the Gaussian assumption is no longer valid, application of the MNI method is questionable. This simulation study compares the performance of the MNI and ordinal imputation regression model for incomplete longitudinal ordinal data for situations covering various numbers of categories of the ordinal outcome, time occasions, sample sizes, rates of missingness, well-balanced, and skewed data.

引用

页码：1311 / 1338

页数：28

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