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相关概念视频

Hazard Ratio01:12

Hazard Ratio

166
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
166
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

441
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
441
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

160
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.
160
Hazard Rate01:11

Hazard Rate

139
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
139
Odds Ratio01:09

Odds Ratio

185
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
185
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.6K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.6K

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相关实验视频

Updated: Jul 26, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

确保在变量选择后对Cox危险比率进行有效推断.

Kelly Van Lancker1,2, Oliver Dukes1, Stijn Vansteelandt1

  • 1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.

Biometrics
|June 23, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,用于在观察性研究中选择混变量,以准确估计因果危险比率. 该方法确保有效的推断,即使使用高维数据,解决现有技术的局限性.

关键词:
有关因果推理的推理.混是一种混.这是一个双重选择.选择后的推断推断.选择变量的选择变量.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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

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相关实验视频

Last Updated: Jul 26, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
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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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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科学领域:

  • 流行病学 流行病学
  • 生物统计学 生物统计学
  • 因果推理因果推理

背景情况:

  • 选择用于混调整的变量对于在观察性研究中估计暴露效应至关重要.
  • 现有的方法在所有样本大小中都缺乏保证的性能.
  • 生存数据带来了独特的挑战,因为混因素可能与审查预测因素不同.

研究的目的:

  • 从观测生存数据中推断条件因果危险比率的可靠程序.
  • 为了应对在存在高维共变量的情况下选择适当的混变量的挑战.
  • 建议在稀疏条件下对暴露效应进行统一有效的假设测试.

主要方法:

  • 在生存分析中进行混调整的一种新而简单的程序.
  • 使用受罚的考克斯回归软件实现.
  • 开发对没有暴露效应的零假设进行统一有效的测试.

主要成果:

  • 提出的方法提供了有效的因果危险比率估计.
  • 模拟结果证明了该方法的有效性,即使使用高维共变量.
  • 该程序在标准稀疏性条件下确保了有效的推断.

结论:

  • 这种新的程序有效地解决了观察性生存研究中的混问题.
  • 该方法很实用,可以使用标准软件实现,并且对高维数据具有强大可靠性.
  • 这项工作推进了生存数据分析的因果推理技术.