A Bayesian Framework for Non-Collapsible Models
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A Bayesian Framework for Non-Collapsible Models

Creative Commons 'BY' version 4.0 license

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.

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