Causal Inference in Presence of Intra-Patient Correlation due to Repeated Measurements of Exposure and Outcome in Longitudinal Settings.

ABSTRACT:

Introduction

In causal inference with time-dependent confounding between an exposure and an outcome, the repeated nature of measures is likely to lead to intra-patient correlation and introduces bias in the estimation of the causal effect, even in the absence of unmeasured confounders.

Method

We evaluated the impact of intra-patient correlation on causal effect estimation with g-computation, inverse probability weighting (IPW) and longitudinal targeted maximum likelihood estimator (LTMLE), and compared two ways of accounting for it, using a fixed-effects or a mixed-effects approach. We conducted a simulation analysis under different scenarios for numerous time points and a real-life analysis to investigate the causal effect of pregnancy on neurological disability in multiple

SUBMITTER: Gavoille A 

PROVIDER: S-EPMC11881680 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature