Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

419
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...
419
Randomized Experiments01:13

Randomized Experiments

6.9K
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...
6.9K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

137
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
137
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

177
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...
177

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Pelvic floor complaint-related psychological distress recorded by pelvic physical therapists in the Netherlands: Additional analysis of data from an exploratory file review study.

Open research Europe·2026
Same author

Modelling non-linear personality change surrounding transitions: A review of statistical approaches.

European journal of personality·2026
Same author

Bayes factor hypothesis testing in meta-analyses: Practical advantages and methodological considerations.

Research synthesis methods·2026
Same author

Rigid control of motor unit firing rates in the human tibialis anterior muscle persists during neurofeedback.

Journal of neurophysiology·2026
Same author

To vary or not to vary: A flexible empirical Bayes factor for testing variance components.

The British journal of mathematical and statistical psychology·2026
Same author

Assessing mobile instant messenger networks with donated data.

Social network analysis and mining·2026
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
查看所有相关文章

相关实验视频

Updated: Jun 26, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.1K

贝叶斯多变量后勤回归对优越和劣势的决策在可观测的治疗条件下异质性.

Xynthia Kavelaars1,2, Joris Mulder1, Maurits Kaptein3

  • 1Department of Methodology and Statistics, Tilburg University.

Multivariate behavioral research
|May 11, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的贝叶斯方法来分析随机对照试验,改善治疗效果决策. 它有助于确定从新疗法中受益最多的患者子组,解决治疗异质性的问题.

关键词:
贝叶斯分析是贝叶斯分析.贝叶斯的多变量逻辑回归.波利亚 - 玛有多个依赖变量.小组分析小组分析治疗异质性的异质性

更多相关视频

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

3.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

相关实验视频

Last Updated: Jun 26, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.1K
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

3.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

科学领域:

  • 生物统计学 生物统计学
  • 临床试验 临床试验
  • 卫生研究方法论 卫生研究方法论

背景情况:

  • 治疗效果在个体之间可能有很大差异.
  • 识别受益于特定治疗的患者子组对于个性化医疗至关重要.
  • 由于异质性,现有的方法可能会忽略治疗疗效的关键变化.

研究的目的:

  • 介绍一种新的贝叶斯框架,用于随机对照试验 (RCT) 中的优越性决策.
  • 具体解决多变量二元结果和异质治疗效应的问题.
  • 为了能够更精确地识别受益于新疗法的患者子组.

主要方法:

  • 利用贝叶斯的多变量逻辑回归与波利亚-马扩展.
  • 实现了回归系数的转换到多变量概率尺度.
  • 开发了一种与受控错误率进行治疗比较的决策程序.
  • 包括先验样本大小估计的方法.

主要成果:

  • 数值评估证实,先验样本大小估计在总和子组分析中保持了预期的错误率.
  • 平均和条件治疗效果参数在足够的样本大小下被不偏地估计.
  • 对国际中风试验数据集的分析表明,治疗效应趋于异质,平均效应分析将错过这一趋势.

结论:

  • 建议的贝叶斯方法有效地处理具有多变量二进制结果的RCT中的治疗异质性.
  • 该框架支持强大的优势决策和子组识别.
  • 这种方法提高了检测细微治疗效应的能力,从而导致更明智的临床决策.