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Analysis of longitudinal data from animals with missing values using SPSS

Nature Protocols volume 11, pages 1112–1129 (2016)Cite this article

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Abstract

Testing of therapies for disease or injury often involves the analysis of longitudinal data from animals. Modern analytical methods have advantages over conventional methods (particularly when some data are missing), yet they are not used widely by preclinical researchers. Here we provide an easy-to-use protocol for the analysis of longitudinal data from animals, and we present a click-by-click guide for performing suitable analyses using the statistical package IBM SPSS Statistics software (SPSS). We guide readers through the analysis of a real-life data set obtained when testing a therapy for brain injury (stroke) in elderly rats. If a few data points are missing, as in this example data set (for example, because of animal dropout), repeated-measures analysis of covariance may fail to detect a treatment effect. An alternative analysis method, such as the use of linear models (with various covariance structures), and analysis using restricted maximum likelihood estimation (to include all available data) can be used to better detect treatment effects. This protocol takes 2 h to carry out.

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Figure 1: Flowchart showing five-stage approach to analyzing longitudinal data in cases where some data are missing.
Figure 2: Screenshots showing arrangement of data in SPSS.
Figure 3: Screenshots showing SPSS windows involved in specification of a model using the MIXED procedure.
Figure 4: Screenshots showing SPSS windows involved in defining the model.
Figure 5: Screenshots showing SPSS windows involved in developing the linear model.
Figure 6: Screenshot showing SPSS Syntax window defining linear model estimated using REML and with two additional lines of code requesting pairwise comparisons for the interaction of group by wave.
Figure 7: Screenshot of SPSS output.
Figure 8: Screenshot of SPSS output.

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Acknowledgements

This work was funded by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 309731, as well as by a Research Councils UK Academic Fellowship and by the Medical Research Council (MRC; G0600998) and the British Pharmacological Society (BPS)'s Integrative Pharmacology Fund. This study was also supported by a Capacity Building Award in Integrative Mammalian Biology funded by the Biotechnology and Biological Sciences Research Council, BPS, Higher Education Funding Council for England, Knowledge Transfer Partnerships and Scottish Funding Council. We are grateful to two anonymous reviewers for their detailed suggestions that have much improved our manuscript, in particular for the advice regarding use of the GENLINMIXED procedure.

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Authors and Affiliations

  1. Wolfson Centre for Age-Related Diseases, King's College London, London, UK

    Denise A Duricki, Sara Soleman & Lawrence D F Moon

  2. Centre for Integrative Biology, King's College London, London, UK

    Denise A Duricki & Lawrence D F Moon

Authors
  1. Denise A Duricki
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  2. Sara Soleman
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  3. Lawrence D F Moon
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Contributions

L.D.F.M. performed the analysis and wrote the manuscript. D.A.D. obtained the data used in the case study. D.A.D. and S.S. edited and improved the manuscript.

Corresponding author

Correspondence to Lawrence D F Moon.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Tutorial (PDF 10608 kb)

Supplementary Data 1

SPSS Syntax and data files. (ZIP 23 kb)

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Duricki, D., Soleman, S. & Moon, L. Analysis of longitudinal data from animals with missing values using SPSS. Nat Protoc 11, 1112–1129 (2016). https://doi.org/10.1038/nprot.2016.048

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