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相关概念视频

The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

373
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...
373
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

134
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.
134
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

441
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
441
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

195
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...
195
Censoring Survival Data01:09

Censoring Survival Data

97
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...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

149
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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相关实验视频

Updated: Jul 7, 2025

An R-Based Landscape Validation of a Competing Risk Model
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从多个站点的垂直分布数据中学习:对于Cox比例危险模型的高效隐私保护算法,具有可变选择.

Guanhong Miao1, Lei Yu2, Jingyun Yang2

  • 1Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA; Center for Genetic Epidemiology and Bioinformatics, University of Florida, Gainesville, FL, USA; Department of Biostatistics, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.

Journal of biomedical informatics
|December 24, 2023
PubMed
概括
此摘要是机器生成的。

本研究为规范化的考克斯模型引入了一种保护隐私的无损分布式算法,使得在联合学习环境中准确分析时间到事件数据而不会损害患者数据.

关键词:
考克斯的比例危险模型.分布式算法 分布式算法保护隐私 保护隐私 保护隐私变量选择 变量选择垂直分区是指垂直的分区.

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相关实验视频

Last Updated: Jul 7, 2025

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科学领域:

  • 分布式计算 分布式计算
  • 生物统计学 生物统计学
  • 机器学习 机器学习

背景情况:

  • 联合学习可以在没有直接数据共享的情况下进行协作模型培训.
  • 分布式数据对传统的统计建模提出了挑战,特别是对于生存分析.
  • 保护隐私的方法对于处理敏感健康数据至关重要.

研究的目的:

  • 开发一个无损的分布式算法,用于调整的Cox比例危险模型,具有变量选择.
  • 支持生存分析中垂直分布的数据的联合学习.
  • 为了实现准确的时间到事件数据建模,而不会损害患者的隐私.

主要方法:

  • 建议基于循环坐标下降的新型分布式算法.
  • 中间统计数据在本地计算,并交换模型更新,保护数据隐私.
  • 该算法使用模拟和现实世界阿尔茨海默氏症痴呆风险预测数据 (ROSMAP) 进行了评估.

主要成果:

  • 分布式算法实现了对时间到事件数据的隐私保护变量选择,与集中式方法相比,没有精度损失.
  • 模拟证实了分析高维数据集的高效率.
  • 现实世界分析显示,与现有的隐私保护模型相比,阿尔茨海默氏症痴呆风险预测准确度和计算效率有所提高.

结论:

  • 开发的算法是无损的,保护隐私,并且有效地将规范化的Cox模型与分布式数据相匹配.
  • 它为分布式时间到事件数据建模提供了一个实用的解决方案.
  • 这种方法促进了协作研究,同时保持了数据安全性和分析完整性.