関連する実験動画

Updated: Sep 10, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

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生存アウトカムを持つ高次元メディエーション分析のための選択後の推論

Tzu-Jung Huang1, Zhonghua Liu2, Ian W McKeague2

  • 1Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.

Scandinavian journal of statistics, theory and applications
|August 25, 2025
PubMed
まとめ

研究者らは,高次元データにおける因果的なメディエーターを特定するための新しい統計的方法を開発し,病気の経路を理解するために不可欠です. このアプローチは,潜在的な仲介者を選択した後に有効な推論を可能にし,ゲノミクスにおける因果推論を進めます.

キーワード:
因果的推論ファミリー・ワイド・エラー・レート制御調停分析についてマルチテスト非標準アシンプトティック選択後の推論右から検閲されたデータ

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Introduction To Survival Analysis01:18

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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