An Overview of Longitudinal Data Analysis Methods for Neurological Research

The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the dependent variable on a time measure, (2) analyzing a single summary subject level number that indexes changes for each subject and (3) a general linear model approach with a fixed-subject effect, should be reserved for quick, simple or preliminary analyses. We advocate the general use of mixed-random and fixed-effect regression models for analyses of most longitudinal clinical studies. Under restrictive situations or to provide validation, we recommend: (1) repeated-measure analysis of covariance (ANCOVA), (2) ANCOVA for two time points, (3) generalized estimating equations and (4) latent growth curve/structural equation models.

[1]  R. Kay.  The Analysis of Survival Data , 2012 .

[2]  Nicholas T. Longford,et al.  Random Coefficient Models , 1994, International Encyclopedia of Statistical Science.

[3]  H. Blossfeld.  Event History Analysis , 2011, International Encyclopedia of Statistical Science.

[4]  A. Atri,et al.  Presence of extrapyramidal motor signs predict worse cognitive and functional trajectory of decline in mild-to-moderate Alzheimer's , 2010, Alzheimer's & Dementia.

[5]  D. Adamis.  Statistical methods for analysing longitudinal data in delirium studies , 2009, International review of psychiatry.

[6]  A. Atri,et al.  Long-term Course and Effectiveness of Combination Therapy in Alzheimer Disease , 2008, Alzheimer disease and associated disorders.

[7]  J. Growdon,et al.  Plasma amyloid beta-protein and C-reactive protein in relation to the rate of progression of Alzheimer disease. , 2008, Archives of neurology.

[8]  Comparison Levine,et al.  Quantitative Applications in the Social Sciences , 2006 .

[9]  Michael C Neale,et al.  People are variables too: multilevel structural equations modeling. , 2005, Psychological methods.

[10]  G. Molenberghs.  Applied Longitudinal Analysis , 2005 .

[11]  J. Twisk,et al.  Longitudinal Data Analysis. A Comparison Between Generalized Estimating Equations and Random Coefficient Analysis , 2003, European Journal of Epidemiology.

[12]  Bengt Muthén,et al.  Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data , 2004 .

[13]  Edward C. Chao,et al.  Generalized Estimating Equations , 2003, Technometrics.

[14]  Bengt Muthén,et al.  Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran (2003). , 2003, Psychological methods.

[15]  J. Teresi,et al.  Some Statistical Issues in the Analyses of Data From Longitudinal Studies of Elderly Chronic Care Populations , 2002, Psychosomatic medicine.

[16]  W. Pan.  Akaike's Information Criterion in Generalized Estimating Equations , 2001, Biometrics.

[17]  L. Edwards.  Modern statistical techniques for the analysis of longitudinal data in biomedical research , 2000, Pediatric pulmonology.

[18]  Leland Wilkinson,et al.  Statistical Methods in Psychology Journals Guidelines and Explanations , 2005 .

[19]  M Palta,et al.  Latent variables, measurement error and methods for analysing longitudinal binary and ordinal data. , 1999, Statistics in medicine.

[20]  Judith D. Singer,et al.  Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models , 1998 .

[21]  B. Everitt,et al.  Analysis of longitudinal data , 1998, British Journal of Psychiatry.

[22]  Jan de Leeuw,et al.  Introducing Multilevel Modeling , 1998 .

[23]  P. J. Jennings,et al.  Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging , 1997, Human brain mapping.

[24]  Larry Hatcher,et al.  A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling , 1994 .

[25]  R M Elashoff,et al.  Analysis of longitudinal data: random coefficient regression modelling. , 1994, Statistics in medicine.

[26]  S D Imber,et al.  Some conceptual and statistical issues in analysis of longitudinal psychiatric data. Application to the NIMH treatment of Depression Collaborative Research Program dataset. , 1993, Archives of general psychiatry.

[27]  J A Yesavage,et al.  The Methodology of Studying Decline in Alzheimer's Disease , 1993, Journal of the American Geriatrics Society.

[28]  E. Ziegel.  Statistical Methods for Survival Data Analysis , 1993 .

[29]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[30]  K Y Liang,et al.  An overview of methods for the analysis of longitudinal data. , 1992, Statistics in medicine.

[31]  P. Albert,et al.  Models for longitudinal data: a generalized estimating equation approach. , 1988, Biometrics.

[32]  D. Francis.  An introduction to structural equation models. , 1988, Journal of clinical and experimental neuropsychology.

[33]  H. Meltzer,et al.  Importance of adjusting for correlated concomitant variables in psychiatric research , 1988, Psychiatry Research.

[34]  Elisa T. Lee,et al.  Statistical Methods for Survival Data Analysis , 1994, IEEE Transactions on Reliability.

[35]  T. Lorei.  Statistical and Methodological Advances in Psychiatric Research—edited by Robert D. Gibbons, Ph.D., and Maurice W. Dysken, M.D.; SP Medical & Scientific Books, New York, 1983, 184 pages, $29.95 , 1984 .

[36]  J. Locascio,et al.  A Re-Analysis of "Lord's Paradox" , 1983 .

[37]  J. Locascio,et al.  The Cross-Lagged Correlation Technique: Reconsideration in Terms of Exploratory Utility, Assumption Specification and Robustness , 1982 .

[38]  John M. Gottman,et al.  Time Series Analysis: A Comprehensive Introduction for Social Scientists. , 1983 .

[39]  Richard A. Berk,et al.  Applied Time Series Analysis for the Social Sciences , 1980 .

[40]  P. Shrout.  Quasi-experimentation: Design and analysis issues for field settings: by Thomas D. Cook and Donald T. Campbell. Chicago: Rand McNally, 1979 , 1980 .

[41]  T. Cook,et al.  Quasi-experimentation: Design & analysis issues for field settings , 1979 .

[42]  H. Huynh,et al.  Estimation of the Box Correction for Degrees of Freedom from Sample Data in Randomized Block and Split-Plot Designs , 1976 .

[43]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[44]  Michael D. Geurts,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[45]  G. Freytag.  [CORRELATION AND CAUSALITY]. , 1964, Psychiatrie, Neurologie, und medizinische Psychologie.

[46]  S. Geisser,et al.  On methods in the analysis of profile data , 1959 .