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相关概念视频

Censoring Survival Data01:09

Censoring Survival Data

256
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...
256
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

200
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.
200
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

582
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...
582
Survival Tree01:19

Survival Tree

166
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...
166
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

327
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
327
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

303
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...
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相关实验视频

Updated: Sep 19, 2025

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

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Published on: October 23, 2020

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对于大规模的考克斯模型的最佳子采样设计,使用受审查的数据.

Shiqi Liu1, Zilong Xie2, Ming Zheng1

  • 1Department of Statistics and Data Science, School of Management, Fudan University, Shanghai, People's Republic of China.

Journal of applied statistics
|June 2, 2025
PubMed
概括

最佳的部分采样设计可以提高大型生存数据集的效率. 这些方法可以降低计算成本和存储需求,同时保持准确的考克斯模型估计.

关键词:
62D05 这是一个很大的问题.62N02 它们是什么?被审查的数据是被审查的数据.估计的准确性估计的准确性.相反的概率权衡.相称危险模型的比例危险模型.部分采样设计设计

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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

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相关实验视频

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科学领域:

  • 生物统计学 生物统计学
  • 数据科学数据科学数据科学
  • 统计建模 统计建模

背景情况:

  • 大规模的生存数据分析带来了计算和存储方面的挑战.
  • 在生存分析中,正确审查的数据是常见的,需要专门的统计方法.
  • 考克斯的比例危险模型是分析生存数据的标准工具.

研究的目的:

  • 根据考克斯模型,为大量的生存数据提出最佳的亚取样设计.
  • 开发适应性设计,优化估计准确性或最大限度地减少估计器方差.
  • 为分析大型生存数据集提供计算效率高的方法.

主要方法:

  • 开发了最佳的分样设计,利用结果和共变量信息.
  • 采用逆概率权重来对子样本数据进行参数估计.
  • 针对不同的估计目标,研究了适应性设计的变化.

主要成果:

  • 拟议的估计器是一致的,并且在异常分布上具有正常分布.
  • 最佳的部分采样设计产生了比统一的部分采样更有效的估计器.
  • 与完整数据分析相比,亚样本显著降低了计算负载和存储成本.

结论:

  • 最佳的部分采样设计为分析大量生存数据提供了有效的策略.
  • 提出的方法为大数据生存分析中减少资源需求提供了实际解决方案.
  • 这些技术提高了对大型复杂数据集的考克斯模型分析的可行性.