A Bayesian Framework for Non-Collapsible Models

TL;DR

This paper introduces a Bayesian approach using Dirichlet process mixtures to estimate covariate effects in non-collapsible models, addressing a complex statistical challenge with practical applications.

Contribution

It proposes a novel Bayesian method for non-collapsible models based on Dirichlet process mixtures, enhancing estimation accuracy and flexibility.

Findings

Method performs well on synthetic data.

Sensitive to different modeling settings.

Applied successfully to real patient data.

Abstract

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.