Research output: Contribution to journal › Article › peer-review
Original language | English |
---|---|
Pages (from-to) | 1112-1129 |
Number of pages | 18 |
Journal | Nature Protocols |
Volume | 11 |
Issue number | 6 |
Early online date | 19 May 2016 |
DOIs | |
Accepted/In press | 4 Jan 2016 |
E-pub ahead of print | 19 May 2016 |
Published | 1 Jun 2016 |
Analysis of longitudinal data_DURICKI_Firstonline19May2016_GREEN AAM
Analysis_of_longitudinal_data_DURICKIFirstonline19may2016GREENAAM.pdf, 473 KB, application/pdf
Uploaded date:24 May 2016
Version:Accepted author manuscript
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.
King's College London - Homepage
© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454