Causal mediation analysis decomposition of between-hospital variance

TL;DR

This paper introduces a method combining causal mediation analysis with variance decomposition to understand how different pathways contribute to between-hospital differences in healthcare quality indicators.

Contribution

It develops a causal mediation variance decomposition framework for hospital performance analysis, integrating generalized linear mixed models for estimation.

Findings

The proposed estimators perform well in simulation studies.

Application to Ontario kidney cancer data illustrates the method's practical utility.

The approach effectively separates direct and mediated effects in hospital performance variation.

Abstract

Causal variance decompositions for a given disease-specific quality indicator can be used to quantify differences in performance between hospitals or health care providers. While variance decompositions can demonstrate variation in quality of care, causal mediation analysis can be used to study care pathways leading to the differences in performance between the institutions. This raises the question of whether the two approaches can be combined to decompose between-hospital variation in an outcome type indicator to that mediated through a given process (indirect effect) and remaining variation due to all other pathways (direct effect). For this purpose, we derive a causal mediation analysis decomposition of between-hospital variance, discuss its interpretation, and propose an estimation approach based on generalized linear mixed models for the outcome and the mediator. We study the performance of the estimators in a simulation study and demonstrate its use in administrative data on kidney cancer care in Ontario.