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相关概念视频

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

634
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...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

132
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
132
Probability Distributions01:32

Probability Distributions

7.9K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
7.9K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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相关实验视频

Updated: Sep 18, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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贝叶斯的双变治愈率模型使用高斯的合体.

Seoyoon Cho1, Matthew A Psioda2, Joseph G Ibrahim3

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Lifetime data analysis
|June 24, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了针对多个时间到事件结果的新疗效率模型,解决了黑色素瘤等疾病传统生存分析的局限性. 这种新的方法准确地模拟了一个治愈的细分群体中的依赖事件.

关键词:
两变的生存模型.治愈率模型中的治愈率模型.黑色素瘤临床试验临床试验截断的高斯式形.

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科学领域:

  • 生物统计学 生物统计学
  • 生存分析的分析.
  • 医学统计 医学统计

背景情况:

  • 传统的生存模型假设所有个体都面临某一事件的风险.
  • 治愈率模型是必要的,当一个人口子集不容易发生事件,如在黑色素瘤治疗.
  • 在瘤学中,对多个时间到事件结果的联合建模至关重要.

研究的目的:

  • 为多个时间到事件结果提出一个新的联合模型,其中包含一个治疗结构.
  • 在治愈人口的场景中解决传统生存模型的局限性.
  • 提供一种方法来分析依赖于时间与事件的结果在治疗的存在下.

主要方法:

  • 开发一个联合模型,使用一种新的截断高斯偶数来实现两种时间到事件结果.
  • 共同模型的制定直接基于时间到事件的结果,不取决于治愈状态.
  • 通过copula的相关性矩阵模拟结果之间的依赖性.
  • 使用马尔科夫链蒙特卡洛程序进行模型装配.

主要成果:

  • 拟议的模型有效地处理多个时间到事件的结果,具有治愈结构.
  • 模拟研究证明了该方法的性能.
  • 使用黑色素瘤临床试验数据的真实数据分析验证了该模型的实用性.
  • 与独立模型的比较强调了联合方法的好处.

结论:

  • 基于Gaussian copula的关节模型提供了一个有价值的工具,用于分析时间到事件数据的治疗分数.
  • 这种多变量方法特别适用于具有多个终点的瘤学研究.
  • 这种方法提供了一个比独立模型更准确的依赖结构,当疗法存在时.