Výsledky vyhledávání - "Ruberg, Stephen J."

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Accurate collection of reasons for treatment discontinuation to better define estimands in clinical trials

Autor: Qu, Yongming, White, Robin D., Ruberg, Stephen J.

Background: Reasons for treatment discontinuation are important not only to understand the benefit and risk profile of experimental treatments, but also to help choose appropriate strategies to handle intercurrent events in defining estimands. The cu

Externí odkaz: http://arxiv.org/abs/2206.01659

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Selection bias in the treatment effect for a principal stratum

Autor: Qu, Yongming, Ruberg, Stephen J., Luo, Junxiang, Lipkovich, Ilya

Estimation of treatment effect for principal strata has been studied for more than two decades. Existing research exclusively focuses on the estimation, but there is little research on forming and testing hypotheses for principal stratification-based

Externí odkaz: http://arxiv.org/abs/2112.09541

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Assessing the commonly used assumptions in estimating the principal causal effect in clinical trials

Autor: Qu, Yongming, Lipkovich, Ilya, Ruberg, Stephen J.

In clinical trials, it is often of interest to understand the principal causal effect (PCE), the average treatment effect for a principal stratum (a subset of patients defined by the potential outcomes of one or more post-baseline variables). Commonl

Externí odkaz: http://arxiv.org/abs/2111.10938

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Estimating the treatment effect for adherers using multiple imputation

Autor: Luo, Junxiang, Ruberg, Stephen J., Qu, Yongming

Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonisation (ICH)-E9 addendum (R1), intercurrent events (ICEs) need

Externí odkaz: http://arxiv.org/abs/2102.03499

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Implementation of Tripartite Estimands Using Adherence Causal Estimators Under the Causal Inference Framework

Autor: Qu, Yongming, Luo, Junxiang, Ruberg, Stephen J.

Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is rel

Externí odkaz: http://arxiv.org/abs/2005.14624

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