Statistical approaches for analyzing immunologic data of repeated observations: a practical guide.
Translational research not only encompasses transitioning from animal to human models but also must address the greater heterogeneity of humans when designing and analyzing experiments. Appropriate study designs can address heterogeneity through a priori data collection, and taking repeated measures can improve the power and efficiency of a study to detect clinically meaningful differences. Although common in other areas of biomedical research, modern statistical methods using repeated measurements on the same subject and accounting for their potential correlations are not widely utilized in immunologic studies. To highlight these analytic issues, we present a practical guide to understanding and applying analytic methods from commonly used T-tests without adjusting for multiple comparisons to mixed models with subject-specific adjustments for correlations using our data on Toll-like receptor-induced cytokine production in monocytes from young and older adults.
Sections of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.
Alexander
A
Panda
Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.
Shu
S
Chen
Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT 06520, USA.
Albert C
AC
Shaw
Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.
Heather G
HG
Allore
D000328:Adult; D000818:Animals; D035843:Biomedical Research / Q000379:methods*; D056808:Biostatistics / Q000379:methods*; D006801:Humans; D018448:Models, Immunological; D009000:Monocytes / Q000276:immunology*
Heterogeneity; Mixed model; Multiple comparisons; Repeated measurement