A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study

A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study

Abstract Background Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection. Traditional multiple imputation (MI) methods (fully conditional specification (FCS) and multivariate normal imputation...

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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 2017-07-01
Series:BMC Medical Research Methodology
Subjects:
Fully conditional specification
Longitudinal data
Missing data
Multiple imputation
Multivariate normal imputation
Non-linear trajectory
Online Access:http://link.springer.com/article/10.1186/s12874-017-0372-y

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