Analysis of longitudinal data from animals with missing values using SPSS

TY - JOUR

T1 - Analysis of longitudinal data from animals with missing values using SPSS

AU - Duricki, Denise A

AU - Soleman, Sara

AU - Moon, Lawrence D F

PY - 2016/6/1

Y1 - 2016/6/1

N2 - 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.

AB - 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.

U2 - 10.1038/nprot.2016.048

DO - 10.1038/nprot.2016.048

M3 - Article

C2 - 27196723

SN - 1754-2189

VL - 11

SP - 1112

EP - 1129

JO - Nature Protocols

JF - Nature Protocols

IS - 6

ER -