Regression models for binary responses are frequently used in medical research. These models assume that the data are sampled from a specific population, but this assumption is often violated in practice due to the presence of outliers. This study introduces a general framework for regression models based on robust divergences, specifically Density Power Divergence and Gamma Divergence. We discuss specific loss functions derived under certain conditions.Furthermore, we apply this robust method to the problem of treatment effect estimation in randomized clinical trials.