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関連する概念動画

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

196
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
196
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

285
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
285
Survival Tree01:19

Survival Tree

159
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.
 Building a Survival Tree
Constructing a...
159
Censoring Survival Data01:09

Censoring Survival Data

230
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
230
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

260
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,...
260
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

395
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.
The primary goal of survival analysis is to estimate survival time—the time...
395

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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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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

さらに関連する動画

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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関連する実験動画

Last Updated: Sep 10, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

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

Published on: September 11, 2021

4.1K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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14.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

科学分野:

  • バイオ統計学
  • ゲノミクス
  • エピデミオロジー

背景:

  • 曝露と結果の関係を理解するために,特に高次元ゲノムデータにおいて,因果的メディエーターを特定することが不可欠です.
  • 現存する方法は,多くの潜在的なメディエーターを持つ限界的メディエーション効果のための有効な選択後の推論を欠いている.

研究 の 目的:

  • 最大限選択された自然間接効果のための堅固な選択後の推論手順を開発する.
  • 原因経路分析における高次元のメディエーターの課題に取り組むこと

主な方法:

  • 半パラメトリック効率的な影響関数アプローチを使用した.
  • 媒介者の選択を考慮して,アシンプトティックな正常に安定した1段階の推定器を開発した.
  • 経験的なパフォーマンスを評価するためにシミュレーション研究を使用しました.

主要な成果:

  • 提案された方法は,シミュレーションで良好な経験的パフォーマンスを示しています.
  • 肺がんのデータセットにアプローチを成功させた.
  • 肺がん生存に対する喫煙の影響を媒介する複数のDNAメチル化CpGサイトを特定した.

結論:

  • 開発された方法は,高次元メディエーション分析のための有効な選択後の推論を提供します.
  • ゲノム研究における 生物学的経路を明らかにする強力なツールです
  • 病気のリスクと進行に関する新しいバイオマーカーの特定を容易にする.