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相关概念视频

Survival Tree01:19

Survival Tree

88
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...
88
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

251
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...
251
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

201
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...
201
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

450
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...
450
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

136
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.
136
Randomized Experiments01:13

Randomized Experiments

7.0K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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相关实验视频

Updated: Jul 12, 2025

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

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随机生存森林具有多变量纵向内源共变量.

Anthony Devaux1,2,3, Catherine Helmer1, Robin Genuer4

  • 1Univ. Bordeaux, INSERM, BPH, U1219, Bordeaux, France.

Statistical methods in medical research
|October 27, 2023
PubMed
概括

这项研究引入了DynForest,这是一种针对个性化医学的新方法,可以使用患者病史预测临床事件风险. DynForest有效地处理复杂的,时间依赖的数据,以便更准确地预测风险.

关键词:
个体动态预测个体动态预测竞争的风险竞争的风险.纵向数据 纵向数据 纵向数据多变量预测器的多变量预测器随机生存森林 随机生存森林幸存率数据 幸存率数据

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科学领域:

  • 生物统计学 生物统计学
  • 个性化医疗是个性化的医疗.
  • 医疗保健中的机器学习

背景情况:

  • 从完整的病史中预测个体临床事件风险对于个性化医学至关重要.
  • 现有的分析方法与许多依赖时间的预测器作斗争,这些预测器具有不规则的,容易出错的测量结果,并且会受到事件的信息截断的影响.

研究的目的:

  • 扩大具有竞争风险的随机生存森林,以使用内源纵向预测器预测事件概率.
  • 开发和评估一种在R包DynForest中实现的方法,能够处理复杂的患者病史数据以进行动态风险预测.

主要方法:

  • 扩展具有竞争风险的随机生存森林 (DynForest),以结合时间依赖的纵向预测因素.
  • 在每个树节点上使用混合模型将内部转换的时间依赖预测器转换为时间固定的特征.
  • 通过在树上平均叶子特定的阿伦-约翰森估计器来计算单个事件概率.

主要成果:

  • DynForest证明了对临床事件的有效预测,超过了使用两个预测器的联合建模方法.
  • 在处理大量纵向预测指标时,该方法被证明优于回归校准,并考虑了信息截断.
  • 在痴呆症研究中的一个应用展示了DynForest在从多式联络标记器开发动态预测工具中的实用性.

结论:

  • DynForest为个性化医学中的动态风险预测提供了一个强大的框架,有效地管理复杂的纵向数据.
  • 该方法允许在预测模型中量化标记者的重要性,有助于临床解释.
  • DynForest为开发先进的临床预测模型提供了宝贵的工具,特别是在痴呆症研究等领域.