In this paper, we discuss the non-collapsibility concept and propose a new
approach based on Dirichlet process mixtures to estimate the conditional effect
of covariates in non-collapsible models. Using synthetic data, we evaluate the
performance of our proposed method and examine its sensitivity under different
settings. We also apply our method to real data on access failure among
hemodialysis patients
