Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study

Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study

Abstract Background Longitudinal categorical variables are sometimes restricted in terms of how individuals transition between categories over time. For example, with a time-dependent measure of smoking categorised as never-smoker, ex-smoker, and current-smoker, current-smokers or ex-smokers cannot...

Full description

Bibliographic Details
Main Authors: Anurika Priyanjali De Silva, Margarita Moreno-Betancur, Alysha Madhu De Livera, Katherine Jane Lee, Julie Anne Simpson
Format: Article
Language:English
Published: BMC 2019-01-01
Series:BMC Medical Research Methodology
Subjects:
Fully conditional specification
Longitudinal categorical data
Missing data
Multiple imputation
Multivariate normal imputation
Restricted transitions
Online Access:http://link.springer.com/article/10.1186/s12874-018-0653-0

Internet

http://link.springer.com/article/10.1186/s12874-018-0653-0

Similar Items