首页|Application of Causal Inference Methods in the Analysis of Observational Neurosurgical Data: G-Formula and Marginal Structural Model
Application of Causal Inference Methods in the Analysis of Observational Neurosurgical Data: G-Formula and Marginal Structural Model
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NSTL
Elsevier
? 2021 The Author(s)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.
Causal inferenceG-formulaInverse probability weightingMarginal structural modelObservational data
Kawahara T.、Shiba K.、Tsuchiya A.
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Clinical Research Promotion Center University of Tokyo Hospital
Department of Epidemiology Harvard T.H. Chan School of Public Health
Department of Emergency and Critical Care Medicine Tokai University School of Medicine
NSTL
Elsevier
