Random-effects models for analyzing clustered data from a nutrition education intervention.
When evaluating the effects of public health intervention, larger units, or clusters, of individuals are often the unit of randomization and implementation. Ignoring dependency in the data due to clustering can misrepresent intervention effects. Random-effects models (REMs) may be a useful way to analyze such data. The present study compares results of analyses of data from a nutrition intervention program using four different methods: (a) usual multiple regression analysis using individual subject data, (b) usual multiple regression analysis using the classroom cluster as the unit of analysis, (c) two-level REM model with subjects clustered within classrooms, and (d) two-level REM model with subjects clustered within sites.
Woodruff
S I
SI
San Diego State University, USA.
D000328:Adult; D002784:Cholesterol / Q000097:blood; D008076:Cholesterol, HDL / Q000097:blood; D016000:Cluster Analysis; D004041:Dietary Fats / Q000008:administration & dosage; D005260:Female; D006266:Health Education / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D008297:Male; D052756:Nutritional Sciences / Q000193:education*; D011897:Random Allocation; D012680:Sensitivity and Specificity; D014481:United States
D008076:Cholesterol, HDL; D004041:Dietary Fats; D002784:Cholesterol