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相关概念视频

Survival Tree01:19

Survival Tree

73
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...
73
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

180
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...
180
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

197
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...
197
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

162
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...
162
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

111
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.
111
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

390
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...
390

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相关实验视频

Updated: Jun 15, 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

加速和可解释的斜随机生存森林

Byron C Jaeger1, Sawyer Welden1, Kristin Lenoir1

  • 1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|August 26, 2024
PubMed
概括

我们开发了一种更快的斜随机生存森林 (RSF) 和一种新的变量重要性 (VI) 方法. 这种方法提高了计算效率,并准确地确定了生存分析中的重要预测因素.

关键词:
计算效率 计算效率 计算效率监督学习学习 监督学习变量的重要性变量.

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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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

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相关实验视频

Last Updated: Jun 15, 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

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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科学领域:

  • 机器学习 机器学习
  • 生物统计学 生物统计学
  • 计算统计学 计算统计学

背景情况:

  • 斜随机生存森林 (RSF) 为右翼审查的数据提供了高的预测准确性.
  • 标准RSF使用每个树单个预测器,而斜式RSF使用线性组合,增加计算成本.
  • 在斜式RSF中估计变量重要性 (VI) 的方法有限.

研究的目的:

  • 为了提高斜式RSF的计算效率.
  • 引入一种可靠的方法来估计斜式RSF的变量重要性 (VI).
  • 为这些方法提供可访问的R包 (aorsf).

主要方法:

  • 使用牛顿-拉普森评分实现了一个计算效率高的斜式RSF.
  • 开发了一种"否定VI"方法,通过评估预测系数对袋外准确度的影响.
  • 与现有的斜式RSF软件进行基准测试,并通过模拟比较VI方法.

主要成果:

  • 新的斜式RSF实现比现有软件快数百倍,保持预测准确度.
  • "否定VI"在区分相关与无关的数值预测指标方面表现优异,与 VI,Shapley VI和基于ANOVA的VI相比.
  • aorsf R包提供了这些先进的斜式RSF方法的访问权限.

结论:

  • 开发的方法显著提高了斜RSF用于生存分析的速度和可用性.
  • "否定VI"方法提供了一个更准确的方法来评估斜式RSF的变量重要性.
  • 这些进步有助于更广泛地应用复杂的生存建模技术.