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- W2970371236 abstract "Abstract Inverse‐probability‐of‐treatment weighted (IPTW) estimation has been widely used to consistently estimate the causal parameters in marginal structural models, with time‐dependent confounding effects adjusted for. Just like other causal inference methods, the validity of IPTW estimation typically requires the crucial condition that all variables are precisely measured. However, this condition, is often violated in practice due to various reasons. It has been well documented that ignoring measurement error often leads to biased inference results. In this paper, we consider the IPTW estimation of the causal parameters in marginal structural models in the presence of error‐contaminated and time‐dependent confounders. We explore several methods to correct for the effects of measurement error on the estimation of causal parameters. Numerical studies are reported to assess the finite sample performance of the proposed methods." @default.
- W2970371236 created "2019-09-05" @default.
- W2970371236 creator A5005105283 @default.
- W2970371236 creator A5015190140 @default.
- W2970371236 date "2019-08-26" @default.
- W2970371236 modified "2023-10-16" @default.
- W2970371236 title "Inverse‐probability‐of‐treatment weighted estimation of causal parameters in the presence of error‐contaminated and time‐dependent confounders" @default.
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- W2970371236 doi "https://doi.org/10.1002/bimj.201600228" @default.
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