Application of Causal Inference Methods in the Analysis of Observational Neurosurgical Data: G-Formula and Marginal Structural Model

Abstract

Objective: When using observational data to estimate the causal effects of a treatment on clinical outcomes, we need to adjust for confounding. In the presence of time-dependent confounders that are affected by previous treatment, adjustments cannot be made via the conventional regression approach or propensity score-based methods, but requires sophisticated methods called g-methods. We aimed to introduce g-methods to estimate the causal effects of treatment strategies defined by treatment at multiple time points, such as treat 2 days versus treat only day 1 versus never-treat.

Methods: Two g-methods were introduced: the g-formula and inverse probability-weighted marginal structural models. Under exchangeability, consistency, and positivity assumptions, they provide a consistent estimate of the causal effects of the treatment strategy.

Results: Using a numeric example that mimics the observational study data, we presented how the g-formula and inverse probability-weighted marginal structural models can estimate the effect of the treatment strategy.

Conclusions: Both g-formula and inverse probability-weighted marginal structural models can correctly estimate the effect of the treatment strategy under 3 identifiability assumptions, which conventional regression analysis cannot. G-methods may assist in estimating the effect of treatment strategy defined by treatment at multiple time points.

Keywords: Causal inference; G-formula; Inverse probability weighting; Marginal structural model; Observational data.