A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases.

ABSTRACT:

Purpose

Confounding adjustment is required to estimate the effect of an exposure on an outcome in observational studies. However, variable selection and unmeasured confounding are particularly challenging when analyzing large healthcare data. Machine learning methods may help address these challenges. The objective was to evaluate the capacity of such methods to select confounders and reduce unmeasured confounding bias.

Methods

A simulation study with known true effects was conducted. Completely synthetic and partially synthetic data incorporating real large healthcare data were generated. We compared Bayesian adjustment for confounding (BAC), generalized Bayesian causal effect estimation (GBCEE), Group Lasso and Doubly robust estimation, high-dimensional propensity score (h

SUBMITTER: Benasseur I 

PROVIDER: S-EPMC9304306 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature