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Analysis of longitudinal data

Beyond MANOVA

Published online by Cambridge University Press:  03 January 2018

B. S. Everitt
B. S. Everitt*
Affiliation:
Department of Biostatistics and Computing, Institute of Psychiatry, De Crespigny Park, Denmark Hill, London SE5 8AF
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Abstract

Background

Longitudinal data arise frequently in psychiatric investigations, and are most often analysed by multivariate analysis of variance (MANOVA) procedures. However, as routinely applied, the method is not satisfactory, particularly when the data are affected by subjects dropping-out of the study. More suitable methods are now available.

Method

Problems with the MANOVA approach are discussed and the advantages of alternative procedures stressed.

Results

Using MANOVA on complete cases to analyse unbalanced longitudinal data can be seriously misleading. More recently developed methods are far more suitable, but only if the missing values are non-informative.

Conclusions

Routine use of MANOVA for the analysis of longitudinal data, particularly when there is a substantial proportion of drop-outs, is ill advised. Statisticians have considerably enriched the available methodologies during the past decade, and psychiatric researchers dealing with such data should be aware of the advantages of the newer methods.

Type
Review Article
Information
The British Journal of Psychiatry , Volume 172 , Issue 1 , January 1998 , pp. 7 - 10
Copyright
Copyright © 1998 The Royal College of Psychiatrists 

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References

Altman, D. G. & Bland, J. M. (1991) Improving doctors' understanding of statistics. Journal of the Royal Statistical Society (Series A), 154, 223–267.Google Scholar
Carey, V., Zeger, S. L. & Diggle, P. J. (1993) Modelling multivariate binary data with alternating logistic regressions, Biometrika, 80, 517–526.CrossRefGoogle Scholar
Conoway, M. R. (1990) A random effect model for binary data. Biometrics, 46, 371–328.Google Scholar
Davis, C. S. (1991) Semi-parametric and non-parametric methods for the analysis of repeated measurements with applications to clinical trials. Statistics in Medicine, 310, 1959–1980.Google Scholar
Diggle, P. J. (1988) An approach to the analysis of repeated measures. Biometrics, 44, 959–971.CrossRefGoogle Scholar
Diggle, P. J. & Kenward, M. G. (1994) Informative drop-out in longitudinal data analysis. Applied Statistics, 43, 49–93.Google Scholar
Diggle, P. J., Liang, K. & Zeger, S. L. (1994) Analysis of Longitudinal Data. Oxford: Oxford University Press.Google Scholar
Dunn, G. & Everitt, B. S. (1992) Applied Multivariate Data Analysis. London: Arnold.Google Scholar
Everitt, B. S. (1995a) The analysis of repeated measures: a practical review with examples. Statistician, 44, 113–135.CrossRefGoogle Scholar
Everitt, B. S. (1995b) The Cambridge Dictionary of Statistics in the Medical Sciences. Cambridge: Cambridge University Press.Google Scholar
Fitzmaurice, G. M., Laird, N. M. & Rotnitzky, A. G. (1993) Regression models for discrete longitudinal responses. Statistical Science, 8, 248–309.Google Scholar
Gregoire, A. J. P., Kumar, R., Everitt, B. S., et al (1996) Transdermal oestrogen for treatment of severe postnatal depression. Lancet, 347, 930–933.Google Scholar
Hand, D. & Sham, R. (1995) Improving the quality of statistics in psychiatric research. British Journal of Psychiatry, 167, 689–691.Google Scholar
Heagerty, P. J. & Zegar, S. L. (1996) Marginal regression models for clustered ordinal measurements. Journal of the American Statistical Association, 91, 1024–1036.Google Scholar
Jenrich, R. I. & Schluchter, M. D. (1986) Unbalanced repeated measures models with structured covariance matrices. Biometrics, 42, 805–820.Google Scholar
Kemp, R., Everitt, B. S., Hayward, P., et al (1997) A randomised controlled trial of compliance therapy: an 18-month follow-up. British Journal of Psychiatry, in press.Google Scholar
Liang, K. Y. & Zeger, S. L. (1986) Longitudinal data analysis using generalised linear models. Biometrika, 73, 13–22.Google Scholar
Little, R. J. A. & Rubin, D. B. (1987) Statistical Analysis with Missing Data. New York: John Wiley.Google Scholar
Little, R. J. A. & Wang, Y. (1996) Pattern-mixture models for multivariate incomplete data with covariates. Biometrics, 52, 98–111.Google Scholar
Molenberghs, G., Kenward, M. G. & Lesaffre, E. (1997) The analysis of longitudinal data with nonrandom drop-out. Biometrika, 84, 33–44.Google Scholar
Spiegelhalter, D. J., Freedman, L. S. & Parmar, M. K. B. (1993) Applying Bayesian ideas in drug development and clinical trials. Statistics in Medicine, 12, 1501–1512.Google Scholar
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