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Unlocking Insights: Enhanced Analysis of Covariance in General Factorial Designs through Multiple Contrast Tests under Variance Heteroscedasticity

Matthias Becher · Ludwig A. Hothorn · Frank Konietschke首次提交 2024-04-22 · 修订 2024-04-23 · v2arXiv预印本

异方差下一般析因设计的协方差分析：多重对比检验AI 解读

A common goal in clinical trials is to conduct tests on estimated treatment effects adjusted for covariates such as age or sex. Analysis of Covariance (ANCOVA) is often used in these scenarios to test the global null hypothesis of no treatment effect using an $F$ -test. However, in several samples, the $F$ -test does not provide any information about individual null hypotheses and has strict assumptions such as variance homoscedasticity. We extend the method proposed by…A common goal in clinical trials is to conduct tests on estimated treatment effects adjusted for covariates such as age or sex. Analysis of Covariance (ANCOVA) is often used in these scenarios to test the global null hypothesis of no treatment effect using an $F$-test. However, in several samples, the $F$-test does not provide any information about individual null hypotheses and has strict assumptions such as variance homoscedasticity. We extend the method proposed by…

微观 · 因果推断 Bootstrap 与重抽样多重检验与同时推断

关联记录被引 0 次 ↗OpenAlex · 核验 2026-09-30

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