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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2018

Open Access 01-12-2018 | Research article

A comparison of multiple imputation methods for missing data in longitudinal studies

Authors: Md Hamidul Huque, John B. Carlin, Julie A. Simpson, Katherine J. Lee

Published in: BMC Medical Research Methodology | Issue 1/2018

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Abstract

Background

Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard) and joint multivariate normal imputation (JM-MVN), which treat repeated measurements as distinct variables, and various extensions based on generalized linear mixed models. Although these MI approaches have been implemented in various software packages, there has not been a comprehensive evaluation of the relative performance of these methods in the context of longitudinal data.

Method

Using both empirical data and a simulation study based on data from the six waves of the Longitudinal Study of Australian Children (N = 4661), we investigated the performance of a wide range of MI methods available in standard software packages for investigating the association between child body mass index (BMI) and quality of life using both a linear regression and a linear mixed-effects model.

Results

In this paper, we have identified and compared 12 different MI methods for imputing missing data in longitudinal studies. Analysis of simulated data under missing at random (MAR) mechanisms showed that the generally available MI methods provided less biased estimates with better coverage for the linear regression model and around half of these methods performed well for the estimation of regression parameters for a linear mixed model with random intercept. With the observed data, we observed an inverse association between child BMI and quality of life, with available data as well as multiple imputation.

Conclusion

Both FCS-Standard and JM-MVN performed well for the estimation of regression parameters in both analysis models. More complex methods that explicitly reflect the longitudinal structure for these analysis models may only be needed in specific circumstances such as irregularly spaced data.
Appendix
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Literature
1.
Diggle P, Heagerty P, Liang KY, Zeger S. Analysis of longitudinal data. Oxford: Oxford University Press; 2013.
2.
Fitzmaurice GM, Laird NM, Ware JH. Applied longitudinal analysis. Hoboken: Wiley; 2012.
3.
4.
Little RJ, Rubin DB. Statistical analysis with missing data. Hoboken: Wiley; 1987.
5.
6.
Rezvan PH, Lee KJ, Simpson JA. The rise of multiple imputation: a review of the reporting and implementation of the method in medical research. BMC Med Res Methodol. 2015;15(1):30–43.CrossRef
7.
SAS Institute, Base SAS 9. 4 Procedures Guide: Statistical Procedures. Cary: SAS Institute; 2014.
8.
Stata Corporation, Stata statistical software, Release 13, College Station, Texas, TX, USA. 2013.
9.
R Core Team, R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. 2013.
10.
Raghunathan TE, Lepkowski JM, Van Hoewyk J, Solenberger P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey methodology. 2001;27(1):85–96.
11.
12.
Schafer JL. Analysis of incomplete multivariate data. New York: Chapman & Hall; 1997.
13.
Van Buuren S, Brand JP, Groothuis-Oudshoorn C, Rubin DB. Fully conditional specification in multivariate imputation. J Stat Comput Simul. 2006;76(12):1049–64.CrossRef
14.
Schafer JL, Yucel RM. Computational strategies for multivariate linear mixed-effects models with missing values. J Comput Graph Stat. 2002;11(2):437–57.CrossRef
15.
Goldstein H, Carpenter J, Kenward MG, Levin KA. Multilevel models with multivariate mixed response types. Stat Model. 2009;9(3):173–97.CrossRef
16.
Quartagno M, Carpenter J. Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates. Stat Med. 2015;35(17):2938–54.CrossRefPubMedPubMedCentral
17.
18.
19.
Van Buuren S. Multiple imputation of multilevel data. Handbook of advanced multilevel analysis, Taylor & Francis Group,  New York, USA 2011;173–96.
20.
Nevalainen J, Kenward MG, Virtanen SM. Missing values in longitudinal dietary data: a multiple imputation approach based on a fully conditional specification. Stat Med. 2009;28(29):3657–69.CrossRefPubMed
21.
Enders CK, Mistler SA, Keller BT. Multilevel multiple imputation: a review and evaluation of joint modeling and chained equations imputation. Psychol Methods. 2016;21(2):222–40.CrossRefPubMed
22.
23.
24.
Lüdtke O, Robitzsch A, Grund S. Multiple imputation of missing data in multilevel designs: a comparison of different strategies. Psychol Methods. 2017;22(1):141–65.CrossRefPubMed
25.
26.
Audigier V, White IR, Jolani S, Debray TP, Quartagno M, Carpenter J, et al. Multiple imputation for multilevel data with continuous and binary variables. Stat Sci. 2018;33(2):160–83.CrossRef
27.
Jansen P, Mensah F, Clifford S, Nicholson J, Wake M. Bidirectional associations between overweight and health-related quality of life from 4–11 years: longitudinal study of Australian children. Int J Obes. 2013;37(10):1307–13.CrossRef
28.
Australian Government Department of Families H, Community Services and Indigenous Affairs (FaHCSIA). Growing Up in Australia: the Longitudinal Study of Australian Children: 2010–11 Annual Report. Canberra, Australia: Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) 2012.
29.
Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320(7244):1240–5.CrossRefPubMedPubMedCentral
30.
Feeney R, Desha L, Khan A, Ziviani J, Nicholson JM. Speech and language difficulties along with other child and family factors associated with health related quality of life of Australian children. Appl Res Qual Life. 2016;11(4):1379–97.CrossRef
31.
Bernaards CA, Belin TR, Schafer JL. Robustness of a multivariate normal approximation for imputation of incomplete binary data. Stat Med. 2007;26(6):1368–82.CrossRefPubMed
32.
Zhao E, Yucel RM. Performance of sequential imputation method in multilevel applications. Proceedings in Jonit statistical meetings, Washington DC. 2009.
33.
Audigier V, Resche-Rigon M. Micemd: Multiple imputation by chained equations with multilevel data. R package; 2017.
34.
Horton NJ, Lipsitz SR, Parzen M. A potential for bias when rounding in multiple imputation. Am Stat. 2003;57(4):229–32.CrossRef
35.
Kalaycioglu O, Copas A, King M, Omar RZ. A comparison of multiple-imputation methods for handling missing data in repeated measurements observational studies. J R Stat Soc A Stat Soc. 2016;179(3):683–706.CrossRef
36.
37.
Hughes RA, White IR, Seaman SR, Carpenter JR, Tilling K, Sterne JA. Joint modelling rationale for chained equations. BMC Med Res Methodol. 2014;14(1):28–37.CrossRefPubMedPubMedCentral
38.
Seaman SR, Hughes RA. Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: the general location model. Stat Methods Med Res. 2018;27(6):1603–14.CrossRefPubMed
39.
Murray JS. Multiple imputation: a review of practical and theoretical findings. Stat Sci. 2018;33(2):142–59.CrossRef
40.
Zhao Y, Long Q. Multiple imputation in the presence of high-dimensional data. Stat Methods Med Res. 2016;25(5):2021–35.CrossRefPubMed
41.
Audigier V, Husson F, Josse J. Multiple imputation for continuous variables using a Bayesian principal component analysis. J Stat Comput Simul. 2016;86(11):2140–56.CrossRef
42.
Zhao J, Schafer J. Pan: Multiple imputation for multivariate panel or clustered data. R Foundation for statistical computing; 2013.
43.
Carpenter JR, Goldstein H, Kenward MG. REALCOM-IMPUTE software for multilevel multiple imputation with mixed response types. J Stat Softw. 2011;45(5):1–14.CrossRef
44.
Van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3).
45.
Keller BT, Enders CK. Blimp Software Manual (Version Beta 6.7). Los Angeles, Ca; 2017.
46.
Robitzsch A, Grund S, Henke T. Miceadds: some additional multiple imputation functions, especially for ‘mice’. R package version 1. 7–8. 2016.
Metadata
Title
A comparison of multiple imputation methods for missing data in longitudinal studies
Authors
Md Hamidul Huque
John B. Carlin
Julie A. Simpson
Katherine J. Lee
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2018
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-018-0615-6

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