An empirical likelihood-based method for comparison of treatment effects—Test of equality of coefficients in linear models

To compare two treatment effects, which can be described as the difference of the parameters in two linear models, we propose an empirical likelihood-based method to make inference for the difference. Our method is free of the assumptions of normally distributed and homogeneous errors, and equal sample sizes. The empirical likelihood ratio for the difference of the parameters of interest is shown to be asymptotically chi-squared. Simulation experiments illustrate that our method outperforms the published ones. Our method is used to analyze a data set from a drug study.