Longitudinal studies play an important role in scientific researches. The defining characteristic of the longitudinal studies is that observations are collected from each subject repeatedly over time, or under different conditions. Missing values are common in longitudinal studies. The presence of missing values is always a fundamental challenge since it produces potential bias, even in well controlled conditions. Three different missing data mechanisms are defined; missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Several imputation methods have been developed in literature to handle missing values in longitudinal data. The most commonly used imputation methods include complete case analysis (CCA), mean imputation (Mean), last observation carried forward (LOCF), hot deck (HOT), regression imputation (Regress), K-nearest neighbor (KNN), The expectation maximization (EM) algorithm, and multiple imputation (MI). In this article, a comparative study is conducted to investigate the efficiency of these eight imputation methods under different missing data mechanisms. The comparison is conducted through simulation study. It is concluded that the MI method is the most effective method as it has the least standard errors. The EM algorithm has the largest relative bias. The different methods are also compared via real data application.
| Published in | International Journal of Statistical Distributions and Applications (Volume 3, Issue 4) |
| DOI | 10.11648/j.ijsd.20170304.13 |
| Page(s) | 72-80 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Dropout Missing, Longitudinal Data, Missing Data, Multiple Imputations, Single Imputation
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APA Style
Ahmed Mahmoud Gad, Rania Hassan Mohamed Abdelkhalek. (2017). Imputation Methods for Longitudinal Data: A Comparative Study. International Journal of Statistical Distributions and Applications, 3(4), 72-80. https://doi.org/10.11648/j.ijsd.20170304.13
ACS Style
Ahmed Mahmoud Gad; Rania Hassan Mohamed Abdelkhalek. Imputation Methods for Longitudinal Data: A Comparative Study. Int. J. Stat. Distrib. Appl. 2017, 3(4), 72-80. doi: 10.11648/j.ijsd.20170304.13
AMA Style
Ahmed Mahmoud Gad, Rania Hassan Mohamed Abdelkhalek. Imputation Methods for Longitudinal Data: A Comparative Study. Int J Stat Distrib Appl. 2017;3(4):72-80. doi: 10.11648/j.ijsd.20170304.13
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Download@article{10.11648/j.ijsd.20170304.13,
author = {Ahmed Mahmoud Gad and Rania Hassan Mohamed Abdelkhalek},
title = {Imputation Methods for Longitudinal Data: A Comparative Study},
journal = {International Journal of Statistical Distributions and Applications},
volume = {3},
number = {4},
pages = {72-80},
doi = {10.11648/j.ijsd.20170304.13},
url = {https://doi.org/10.11648/j.ijsd.20170304.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20170304.13},
abstract = {Longitudinal studies play an important role in scientific researches. The defining characteristic of the longitudinal studies is that observations are collected from each subject repeatedly over time, or under different conditions. Missing values are common in longitudinal studies. The presence of missing values is always a fundamental challenge since it produces potential bias, even in well controlled conditions. Three different missing data mechanisms are defined; missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Several imputation methods have been developed in literature to handle missing values in longitudinal data. The most commonly used imputation methods include complete case analysis (CCA), mean imputation (Mean), last observation carried forward (LOCF), hot deck (HOT), regression imputation (Regress), K-nearest neighbor (KNN), The expectation maximization (EM) algorithm, and multiple imputation (MI). In this article, a comparative study is conducted to investigate the efficiency of these eight imputation methods under different missing data mechanisms. The comparison is conducted through simulation study. It is concluded that the MI method is the most effective method as it has the least standard errors. The EM algorithm has the largest relative bias. The different methods are also compared via real data application.},
year = {2017}
}
TY - JOUR T1 - Imputation Methods for Longitudinal Data: A Comparative Study AU - Ahmed Mahmoud Gad AU - Rania Hassan Mohamed Abdelkhalek Y1 - 2017/11/10 PY - 2017 N1 - https://doi.org/10.11648/j.ijsd.20170304.13 DO - 10.11648/j.ijsd.20170304.13 T2 - International Journal of Statistical Distributions and Applications JF - International Journal of Statistical Distributions and Applications JO - International Journal of Statistical Distributions and Applications SP - 72 EP - 80 PB - Science Publishing Group SN - 2472-3509 UR - https://doi.org/10.11648/j.ijsd.20170304.13 AB - Longitudinal studies play an important role in scientific researches. The defining characteristic of the longitudinal studies is that observations are collected from each subject repeatedly over time, or under different conditions. Missing values are common in longitudinal studies. The presence of missing values is always a fundamental challenge since it produces potential bias, even in well controlled conditions. Three different missing data mechanisms are defined; missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Several imputation methods have been developed in literature to handle missing values in longitudinal data. The most commonly used imputation methods include complete case analysis (CCA), mean imputation (Mean), last observation carried forward (LOCF), hot deck (HOT), regression imputation (Regress), K-nearest neighbor (KNN), The expectation maximization (EM) algorithm, and multiple imputation (MI). In this article, a comparative study is conducted to investigate the efficiency of these eight imputation methods under different missing data mechanisms. The comparison is conducted through simulation study. It is concluded that the MI method is the most effective method as it has the least standard errors. The EM algorithm has the largest relative bias. The different methods are also compared via real data application. VL - 3 IS - 4 ER -
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