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相关概念视频

Survival Tree01:19

Survival Tree

159
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...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
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...
86
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

602
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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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

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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...
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Updated: Sep 11, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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结构化贝叶斯回归树模型用于估计分布式滞后效应:R包 dlmtree

Seongwon Im1, Ander Wilson1, Daniel Mork2

  • 1Department of Statistics, Colorado State University, United States of America.

The R journal
|August 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了dlmtree,这是树结构分布式滞后模型 (DLM) 的一个R包. 它简化了分析暴露-结果关系的时间延迟和平滑效应,帮助研究人员进行复杂的统计建模.

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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:

  • 暴露-结果关系往往涉及时间滞后.
  • 分布式滞后模型 (DLM) 估计了这些滞后效应.
  • 自主相关数据需要对滞后效应进行平滑的约束.

研究的目的:

  • 介绍树结构 DLM 的 R 包 dlmtree.
  • 提供先进的DLM技术的用户友好的实现.
  • 促进对暴露-结果关系与时间滞后的分析.

主要方法:

  • 使用树结构分布式滞后模型 (DLM) 框架.
  • 集成扩展用于全面的统计建模.
  • 在R包中提供了用户友好的实现.

主要成果:

  • dlmtree包提供了树结构DLM的无集成.
  • 展示了与模拟数据相匹配,推断和解释.
  • 包括一个用于异质性分析的Shiny应用程序.

结论:

  • dlmtree为研究人员提供了一个全面且易于使用的工具.
  • 能够对时间滞后的曝光-结果关联进行可靠的分析.
  • 方便先进的统计建模和数据可视化.