Causal Forests Versus Inverse Probability of Treatment Weighting to Adjust for Cluster-Level Confounding: A Parametric and Plasmode Simulation Study Based on US Hospital Electronic Health Record Data.

ABSTRACT:

Background

Rapid innovation and new regulations increase the need for post-marketing surveillance of implantable devices. However, complex multi-level confounding related to patient-level and surgeon or hospital covariates hampers observational studies of risks and benefits. We conducted two simulation studies to compare the performance of Causal Forests (CF) versus Inverse Probability of Treatment Weighting (IPTW) to reduce confounding bias in the presence of strong surgeon impact on treatment allocation.

Methods

Two Monte Carlo simulation studies were carried out: (1) Parametric simulations with patients nested in clusters (ratio 10:1, 50:1, 100:1, 200:1, 500:1) and sample size n = 10 000 were conducted with patient and cluster level confounders; (2) Plasmode simulations g

SUBMITTER: Du M 

PROVIDER: S-EPMC12583492 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature