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相关概念视频

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

237
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...
237
Censoring Survival Data01:09

Censoring Survival Data

92
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...
92
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

127
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.
127
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

433
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...
433
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

138
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,...
138
Survival Tree01:19

Survival Tree

85
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...
85

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相关实验视频

Updated: Jul 4, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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用多种脆弱性和多层次生存模型估计反复事件数据的样本大小.

Derek Dinart1,2, Carine Bellera1,2, Virginie Rondeau1,3

  • 1Epicene, University Bordeaux, Inserm, Bordeaux Population Health Research Center, Bordeaux, France.

Journal of biopharmaceutical statistics
|February 9, 2024
PubMed
概括
此摘要是机器生成的。

在临床研究中,计算复发事件的样本大小至关重要. 这项研究分析了使用脆弱模型对反复发生的时间到事件数据的样本大小,发现样本需求增加,异质性更高.

关键词:
样本的大小 样本大小脆弱模型的脆弱性模型联合模型 联合模型随机的,随机的,随机的,随机的,随机的经常性事件 经常性事件

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相关实验视频

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科学领域:

  • 流行病学和临床研究.
  • 生物统计学 生物统计学
  • 对生存分析的分析.

背景情况:

  • 在临床研究中,经常出现住院或骨折等复发性事件.
  • 准确的样本大小估计对于反复事件分析的统计能力至关重要.
  • 在反复事件数据分析中计算样本大小的现有方法各不相同.

研究的目的:

  • 为经常性时间到事件数据提供样本大小要求的深入分析.
  • 为了进行样本大小估计,比较不同的脆弱性模型 (共享,层次,联合).
  • 提供关于优化研究设计的指导,用于反复事件研究.

主要方法:

  • 使用沃尔德型测试统计数据来估计样本大小.
  • 研究了从简单的共同脆弱模型到复杂的多层次生存模型.
  • 进行模拟以评估不同异质性的样本大小.
  • 将该方法应用于AFFIRM-AHF试验数据.

主要成果:

  • 样本大小要求随着反复事件数据的异质性增加而增加.
  • 包括更多的随访时间较短的患者通常比较少的随访时间较长的患者更有效.
  • 不同的脆弱性模型根据研究问题提供了合适的样本大小计算.

结论:

  • 脆弱性模型的选择会影响反复事件的样本大小计算.
  • 研究设计,平衡患者数量和随访持续时间,对于实现所需事件计数至关重要.
  • 本文所介绍的方法为在反复事件研究中确定样本大小提供了一个强大的框架.