• Takuya Kawahara, Koichiro Shiba, and Asuka Tsuchiya.
• Clinical Research Promotion Center, University of Tokyo Hospital, Tokyo, Japan. Electronic address: takuyakawahara@g.ecc.u-tokyo.ac.jp.
• World Neurosurg. 2022 May 1; 161: 310-315.

ObjectiveWhen 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.MethodsTwo 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.ResultsUsing 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.ConclusionsBoth 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.Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.

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