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相关概念视频

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

85
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.
85
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

339
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...
339
Censoring Survival Data01:09

Censoring Survival Data

56
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...
56
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

169
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...
169
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

131
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...
131
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

288
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...
288

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相关实验视频

Updated: May 29, 2025

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

10.1K

DsubCox:用于使用分布式和大规模生存数据的考克斯模型的快速亚抽样算法.

Haixiang Zhang1, Yang Li2, HaiYing Wang3

  • 1Center for Applied Mathematics and KL-AAGDM, 12605 Tianjin University , Tianjin 300072, China.

The international journal of biostatistics
|February 3, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一种用于Cox模型的快速亚抽样方法,使用了大量的生存数据. 这种方法保护隐私,并通过仅传输汇总统计数据来减少计算,从而使大规模的分散数据集能够有效分析.

关键词:
根据L-最佳性标准的标准.分布式学习是一种分布式的学习.大规模的生存数据.最优的部分采样样

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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An R-Based Landscape Validation of a Competing Risk Model
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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科学领域:

  • 生物统计学 生物统计学
  • 计算统计学 计算统计学
  • 生存分析的分析.

背景情况:

  • 来自多中心,分散的来源的大规模生存数据集带来了重大的隐私和计算挑战.
  • 现有的方法可能会与现代生存数据的规模和分布性质作斗争.
  • 有效且保护隐私的统计方法对于分析大规模的健康和研究数据至关重要.

研究的目的:

  • 为考克斯模型提出一个针对大规模,分散的生存数据集量身定制的快速亚抽样程序.
  • 开发一个能够确保隐私保护并减轻计算负担的估计器.
  • 通过传输总结级统计数据,实现有效的数据分析.

主要方法:

  • 为考克斯模型开发了一种新的部分采样程序.
  • 为了指导数据子集的选择,我们得出了最佳的部分采样概率.
  • 拟议估计器的非对称性质被严格确立为可靠的推断.
  • 该方法使用广泛的模拟研究进行了验证,并应用于真实世界的数据集.

主要成果:

  • 拟议的亚抽样估计器有效地处理大规模的,分散的生存数据.
  • 该方法允许通过仅传输总结统计数据来保护隐私.
  • 传送基于亚样本的总结统计数据只需要一轮的沟通.
  • 模拟研究证实了拟议方法的有效性和效率.

结论:

  • 快速亚抽样程序为分析大规模,分散的生存数据提供了有效的解决方案.
  • 这种方法大大降低了计算负担,并加强了隐私保护.
  • 该方法适用于现实世界的应用,正如其在分析美国航空公司数据中的使用所证明的那样.