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相关概念视频

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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相关实验视频

Updated: Sep 10, 2025

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

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选择后推断高维度调解分析与生存结果

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
概括

研究人员开发了一种新的统计方法,用于识别高维数据中的因果媒介,这对于了解疾病途径至关重要. 这种方法可以在选择潜在的调解者后进行有效的推断,从而在基因组学中推进因果推断.

关键词:
因果推理家庭级错误率控制调解分析多次测试非标准的异常选择后的推断权利审查的数据

更多相关视频

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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相关实验视频

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

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

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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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科学领域:

  • 生物统计学
  • 基因组学
  • 流行病学

背景情况:

  • 鉴定因果媒介对于理解暴露结果关系至关重要,尤其是在高维基因组数据中.
  • 现有的方法缺乏有效的选择后推断,用于许多潜在调解者的边际调解效应.

研究的目的:

  • 为了最大限度地选择自然间接效应,开发一个强大的选择后推断程序.
  • 解决因果途径分析中的高维媒介的挑战.

主要方法:

  • 使用半参数有效影响函数方法.
  • 开发了一个稳定的一步估计器,具有非对称的正常性,用于调解器选择.
  • 使用模拟研究来评估经验性表现.

主要成果:

  • 拟议的方法在模拟中表现出良好的实证性能.
  • 这一方法已成功应用于肺癌数据集.
  • 确定了多个DNA甲基化CpG位点,可能介导吸烟对肺癌存活率的影响.

结论:

  • 开发的方法为高维度调解分析提供了有效的选择后推断.
  • 在基因组研究中发现生物通路的强大工具.
  • 有助于识别疾病风险和进展的新生物标志物.