Investigation of the structure and magnitude of time-varying uncontrolled confounding in simulated cohort data analyzed using g-computation.

ABSTRACT:

Background

When estimating the effect of time-varying exposures on longer-term outcomes, the assumption of conditional exchangeability or no uncontrolled confounding extends beyond baseline confounding to include time-varying confounding. We illustrate the structures and magnitude of uncontrolled time-varying confounding in exposure effect estimates obtained from g-computation when sequential conditional exchangeability is violated.

Methods

We used directed acyclic graphs (DAGs) to depict time-varying uncontrolled confounding. We performed simulations and used g-computation to quantify the effects of each time-varying exposure for each DAG type. Models adjusting all time-varying confounders were considered the true (bias-adjusted) estimate. The exclusion of time-varying unco

SUBMITTER: Soohoo M 

PROVIDER: S-EPMC10749778 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature