title: Estimation of group means when adjusting for covariates in generalized linear models.
abstract: Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes.
authors.affiliation: Department of Biometrics, Eli Lilly and Company, Indianapolis, IN, USA.
authors.forename: Yongming
authors.initials: Y
authors.lastname: Qu
authors.forename: Junxiang
authors.initials: J
authors.lastname: Luo
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D012044:Regression Analysis
keywords: hypoglycemic events; logistic regression; negative binomial regression