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Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials

Autor: Gao, Chenyin, Yang, Shu, Shan, Mingyang, Ye, Wenyu Wendy, Lipkovich, Ilya, Faries, Douglas

Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Recent approaches leverage external controls from historical studies or

Externí odkaz: http://arxiv.org/abs/2410.18409

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Improving randomized controlled trial analysis via data-adaptive borrowing

Autor: Gao, Chenyin, Yang, Shu, Shan, Mingyang, Ye, Wenyu, Lipkovich, Ilya, Faries, Douglas

In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balanced randomization is unethical or impractical. However, as external co

Externí odkaz: http://arxiv.org/abs/2306.16642

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Combining Doubly Robust Methods and Machine Learning for Estimating Average Treatment Effects for Observational Real-world Data

Autor: Tan, Xiaoqing, Yang, Shu, Ye, Wenyu, Faries, Douglas E., Lipkovich, Ilya, Kadziola, Zbigniew

Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have been proposed for average treatment effect estimation by combining the

Externí odkaz: http://arxiv.org/abs/2204.10969

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