An approach for quantifying small effects in regression models.
We develop a novel approach for quantifying small effects in regression models. Our method is based on variation in the mean function, in contrast to methods that focus on regression coefficients. Our idea applies in diverse settings such as testing for a negligible trend and quantifying differences in regression functions across strata. Straightforward Bayesian methods are proposed for inference. Four examples are used to illustrate the ideas.
1 Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
Edward J
EJ
Bedrick
2 Sandia National Laboratories, Albuquerque, NM, USA.
Lauren
L
Hund
D000293:Adolescent; D000704:Analysis of Variance; D001499:Bayes Theorem; D035843:Biomedical Research / Q000706:statistics & numerical data; D002096:C-Peptide / Q000097:blood; D002648:Child; D002675:Child, Preschool; D005260:Female; D006304:Health Status; D006801:Humans; D007223:Infant; D008297:Male; D015233:Models, Statistical; D012044:Regression Analysis
D002096:C-Peptide
Analysis of covariance; R-squared; interaction; negligible effects; tests for equivalence