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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

413
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...
413
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

68
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...
68
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
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...
4.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

37
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
37
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

476
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
476

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相关实验视频

Updated: Jun 25, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

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对非参数混合效应模型的贝叶斯回归与形状受限的伯恩斯坦多项式.

Jianhua Ding1, Zhongzhan Zhang2

  • 1Department of Statistics, Shanxi Datong University, Datong, People's Republic of China.

Journal of applied statistics
|May 31, 2024
PubMed
概括

我们为形状受约束的非参数混合效应模型引入了一种新的贝叶斯方法. 这种方法提高了复杂数据模式的统计建模准确性,改善了各种应用中的估计.

科学领域:

  • 统计 统计 统计 统计
  • 生物统计学 生物统计学
  • 计算统计学 计算统计学

背景情况:

  • 非参数混合效应模型广泛应用于各种领域.
  • 现有的方法在处理形状受限制的数据时可能缺乏灵活性.
  • 贝叶斯式方法为复杂的建模提供了一个强大的框架.

研究的目的:

  • 为具有形状约束的非参数混合效应模型开发一种新的贝叶斯估计方法.
  • 在一个层次化的贝叶斯框架内利用形状受约束的伯恩斯坦多项式.
  • 为表现特定功能形式的数据提供灵活和准确的统计工具.

主要方法:

  • 采用了一个层次化的贝叶斯框架.
  • 形状受约束的伯恩斯坦多项式 (BPs) 的特征.
  • 马尔科夫链蒙特卡洛 (MCMC) 方法用于模型配件.
  • 一个截断的正常分布作为BP系数的先验来执行形状约束.

主要成果:

  • 提出的贝叶斯形状受约束的估计器证明了有利的小样本属性.
  • 跨多种功能的模拟研究验证了该方法的性能.
  • 现实世界的数据分析证实了该方法的实际适用性和有效性.
关键词:
伯恩斯坦多项式的多项式马尔科夫链蒙特卡洛采样器形状的限制是形状的限制.截断的正常分布 截断的正常分布

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结论:

  • 开发的贝叶斯方法有效地处理形式受限的非参数混合效应模型.
  • 该方法提供了准确的估计,特别是在小样本场景中.
  • 这种方法为分析具有固有的形状限制的复杂数据提供了有价值的工具.