Causal Inference in Presence of Intra‐Patient Correlation due to Repeated Measurements of Exposure and Outcome in Longitudinal Settings
Résumé
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 sclerosis. Results In simulation analyses, the presence of intra‐patient correlation led to bias in the causal effect estimation with g‐computation, IPW, and LTMLE when this was not accounted for; the bias was smaller for LTMLE. Taking into account intra‐patient correlation with fixed‐effects and mixed‐effects approaches reduced the bias in g‐computation, with lower standard errors for the mixed‐effects approach. Regarding IPW, the fixed‐effects approach suffered from weight stability issues when the number of time points increased, and the mixed‐effects approach provided inconsistent estimates of the intra‐patient correlation in the exposure model. Application to real‐life data yielded results consistent with the simulation study, highlighting the importance of accounting for intra‐patient correlation. Conclusion When analyzing longitudinal data in the presence of time‐dependent confounding using g‐methods, intra‐patient correlation due to repeated measurements of exposure and outcome should be accounted for in the causal reasoning.
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