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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.1K
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...
1.1K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

867
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...
867
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.1K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

456
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.
456
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

1.3K
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...
1.3K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.2K
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...
5.2K

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

Generating synthetic multi-national longitudinal cohorts for clinically grounded HIV research.

Nature communications·2026
Same author

Acute respiratory infection and associated factors among young children presenting to hospital in Sierra Leone.

International health·2026
Same author

Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.

JAMIA open·2026
Same author

Multi-ancestry transcriptome-wide association studies uncover insights into breast cancer genetics and biology.

Nature communications·2026
Same author

Blood transcriptomic signatures predict poor outcomes in drug-susceptible pulmonary TB in Brazil.

American journal of respiratory and critical care medicine·2026
Same author

Estrogen Metabolism-Related Lifestyle Score and Risk of Postmenopausal Breast, Endometrial, and Ovarian Cancers: Findings from Two Large Prospective Cohort Studies.

Cancer prevention research (Philadelphia, Pa.)·2026
Same journal

ggpedigree: Visualizing Pedigrees with 'ggplot2' and 'plotly'.

Journal of open source software·2026
Same journal

ACHR.cu: GPU-accelerated sampling of metabolic networks.

Journal of open source software·2026
Same journal

svZeroDSolver: A modular package for lumped-parameter cardiovascular simulations.

Journal of open source software·2026
Same journal

baysc: An R package for Bayesian survey clustering.

Journal of open source software·2026
Same journal

FastPCA: An R package for fast singular value decomposition.

Journal of open source software·2026
Same journal

Napari-3D-Counter: A manual cell counter for napari.

Journal of open source software·2026
查看所有相关文章

相关实验视频

Updated: Feb 24, 2026

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

3.8K

sleev: 一个半参数概率估计的R包,其中包含变量错误.

Jiangmei Xiong1, Sarah C Lotspeich2, Joey B Sherrill3

  • 1Department of Biostatistics, Vanderbilt University Medical Center, USA.

Journal of open source software
|February 23, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于分析具有测量错误的生物医学数据的R包套. 它有效地实现了子最大概率估计器 (SMLE) 对于具有易出错结果或共变量的两阶段研究.

更多相关视频

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

相关实验视频

Last Updated: Feb 24, 2026

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

3.8K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

科学领域:

  • 生物医学研究的研究.
  • 统计方法学的统计方法.
  • 数据科学是数据科学.

背景情况:

  • 在生物医学研究中常规收集的数据往往包含结果或共变量的测量错误.
  • 两阶段研究设计是常见的,其中仅验证了数据的子样本.
  • 分析容易出错的数据需要专门的统计方法.

研究的目的:

  • 解决对计算效率高和用户友好的工具的需求,用于分析易出错的数据在两阶段研究中.
  • 引入 R 套件,用于实施的最大概率估计器 (SMLE).
  • 为了促进基于半参数概率的推断,对易出错的二进制和连续结果和共变量进行推断.

主要方法:

  • 使用了子最大概率估计器 (SMLE) 方法.
  • 开发了R包,用于实施SMLE进行两阶段研究.
  • 该包处理易出错的二进制和连续结果和共变量.

主要成果:

  • 该R套件提供了一个用户友好的工具,用于应用SMLE.
  • 能够对容易出错的复杂数据进行高效,可靠的分析.
  • 支持对具有测量误差的二进制和连续结果进行分析.

结论:

  • 实验组合有效填补了在两阶段研究中分析易出错的数据的缺口.
  • 它提高了在生物医学研究中使用SMLE的可访问性和效率.
  • 该工具支持广泛的数据类型,包括易出错的响应和共变量.