Speaker：Professor Masataka Taguri (Tokyo Medical University)
Topic：Stable estimation of time-varying treatment effects using an approximate multiply robust estimator
Speaker：Professor Masataka Taguri (Tokyo Medical University)
Date Time：Tue. Dec 19, 2023, 11:00 AM - 12:00 PM
Place： 4F-427, Assembly Building I
Abstract
In longitudinal studies involving time-varying treatments, time-dependent confounding can occur if the time-dependent covariates are influenced by past treatments. Multiply robust estimators, which are based on augmented inverse probability (AIPW) estimating equations, have been proposed to protect against model misspecification(Bang and Robins, 2005; Rotnitzky et al., 2017). However, these estimators have large variances when there is significant variability in inverse probability weights. To address this issue, we propose an approximate multiply robust estimator based on stratification of inverse probability weights.For the point treatment, our proposed method corresponds to the approach combining outcome regression models and propensity score stratification (Lunceford and Davidian, 2004). We will present the results of simulations comparing the proposed estimator with existing estimators.
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