Missing data in longitudinal studies: Cross-sectional multiple imputation provides similar estimates to full-information maximum likelihood

Annals of Epidemiology

Volumn 24, Issue 1, 2014, Pages 75-77

  Ferro, Mark A ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

Latent growth curve model; Longitudinal studies; Missing data; Models; Multiple imputation; Statistical; Structural equation model

Indexed keywords

ANALYSIS OF VARIANCE; ARTICLE; CLINICAL ASSESSMENT; CONTROLLED STUDY; CROSS-SECTIONAL STUDY; DEPENDENT VARIABLE; EPIDEMIOLOGICAL DATA; EXPLORATORY RESEARCH; GROWTH CURVE; INDEPENDENT VARIABLE; LONGITUDINAL STUDY; MAXIMUM LIKELIHOOD METHOD; MEASUREMENT; MEDICAL INFORMATION; MONTE CARLO METHOD; PARAMETERS; PRIORITY JOURNAL; PROBABILITY; SIMULATION; STATISTICAL ANALYSIS;

LATENT GROWTH CURVE MODEL; LONGITUDINAL STUDIES; MISSING DATA; MODELS; MULTIPLE IMPUTATION; STATISTICAL; STRUCTURAL EQUATION MODEL;

CROSS-SECTIONAL STUDIES; DATA INTERPRETATION, STATISTICAL; HUMANS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; PATIENT DROPOUTS; PROBABILITY; RESEARCH DESIGN;

EID: 84890117828     PISSN: 10472797     EISSN: 18732585     Source Type: Journal    
DOI: 10.1016/j.annepidem.2013.10.007     Document Type: Article

References (12)

1. Graham J.W. Missing data analysis: making it work in the real world. Annu Rev Psychol 2009, 60:549-576. PubMed PMID: 18652544. Epub 2008/07/26.
2. Allison P.D. Missing data techniques for structural equation modeling. JAbnorm Psychol 2003, 112(4):545-557. PubMed PMID: 14674868. Epub 2003/12/17.
3. Tseng C.H., Elashoff R., Li N., Li G. Longitudinal data analysis with non-ignorable missing data. Stat Methods Med Res 2012, PubMed PMID: 22637472.
4. Duncan T.E., Duncan S.C., Li F.Z. Acomparison of model- and multiple imputation-based approaches to longitudinal analyses with partial missingness. Structural Equation Modeling-a Multidisciplinary Journal 1998, 5(1):1-21. PubMed PMID: WOS:000208063100001. English.
5. SAS/STAT® 9.22 user's guide 2010, SAS Institute Inc, SAS Institute Inc., Cary.
6. Twisk J., de Vente W. Attrition in longitudinal studies. How to deal with missing data. JClin Epidemiol 2002, 55(4):329-337. PubMed PMID: 11927199.
7. Donders A.R., van der Heijden G.J., Stijnen T., Moons K.G. Review: a gentle introduction to imputation of missing values. JClin Epidemiol 2006, 59(10):1087-1091. PubMed PMID: 16980149.
8. Duncan T.E., Duncan S.C., Stryker L.A. An introduction to latent variable growth curve modeling. Concepts, issues, and applications 2006, Lawrence Erlbaum Associates, Mahwah. 2nd ed.
9. Singh R. Does my structural model represent the real phenomenon? A review of the appropriate use of structural equation modeling (SEM) model fit indices. Mark Rev 2009, 9(3):199-212.
10. Kleinbaum D.G., Kupper L.L., Muller K.E., Nizam A. Applied regression analysis and other multivariable methods 1998, Duxbury Press, Pacific Grove.
11. Nevalainen J., Kenward M.G., Virtanen S.M. Missing values in longitudinal dietary data: a multiple imputation approach based on a fully conditional specification. Stat Med 2009, 28(29):3657-3669. PubMed PMID: 19757484.
12. Welch C., Bartlett J., Petersen I. Application of multiple imputation using the two-fold fully conditional specification algorithm in longitudinal clinical data. Stata J 2013, in press.