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

Bias01:22

Bias

7.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.2K
What are Estimates?01:06

What are Estimates?

8.0K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.0K
Censoring Survival Data01:09

Censoring Survival Data

505
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
505
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.2K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.2K
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

1.1K
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...
1.1K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.5K

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

Improper geometric ferroelectricity at the monolayer limit.

Science advances·2026
Same authorSame journal

Non-random selection with and without bias due to selecting on an exposure.

Epidemiology (Cambridge, Mass.)·2026
Same author

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
Same author

Driver wavelength and intensity dependence of extreme ultraviolet emission from laser-produced tin microdroplet plasmas.

Optics express·2026
Same author

The (Mis)interpretation of Hazard Ratios in Clinical Trials.

Journal of the American Society of Nephrology : JASN·2026
Same author

Association and mediating pathways between intergenerational educational mobility and depressive symptoms: findings from high- and middle-income countries.

BMC medicine·2026

相关实验视频

Updated: Jan 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

构建G计算估计器:选择偏差的两个案例研究

Paul N Zivich1, Haidong Lu2

  • 1From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC.

Epidemiology (Cambridge, Mass.)
|December 4, 2025
PubMed
概括
此摘要是机器生成的。

G计算是一种灵活的流行病学工具,可以适应复杂的因果结构和偏见. 这项研究证明了适应g计算的选择偏差,提供了实际的实施指南.

关键词:
碰撞器的偏差 碰撞器的偏差估计方程中的估计方程.这就是G计算.这就是G-公式.选择偏差是一种选择偏差.

更多相关视频

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.6K

相关实验视频

Last Updated: Jan 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.6K

科学领域:

  • 流行病学 流行病学
  • 因果推理因果推理
  • 生物统计学 生物统计学

背景情况:

  • G计算是解决流行病学偏差的一个有价值的方法.
  • 对复杂的因果结构进行g计算的调整可能具有挑战性.
  • 将因果图转化为估计策略需要仔细考虑.

研究的目的:

  • 为了证明g计算对流行病学中的特定选择偏差场景的适应性.
  • 为实施适应g计算估计器提供实际指导.
  • 探索新型g计算估计器的理论和有限样本特性.

主要方法:

  • 该研究针对两个选择偏差病例调整了g计算:治疗诱导的选择和没有联合调整集的同时发生的偏差.
  • 拟议的估计器以堆叠的估计方程来表达,用于简化理论和应用.
  • 使用模拟来说明调整后的估计器的性能.

主要成果:

  • G计算可以有效地适应在流行病学研究中解决复杂的选择偏差.
  • 堆叠估计方程为实施新型g计算估计器提供了实际框架.
  • 模拟证实了开发的估计器的实用性和特性.

结论:

  • 流行病学家可以将因果识别策略转化为实际的g计算估计器.
  • 提出的方法有助于研究新型因果估计器的理论和有限样本特性.
  • 这项工作增强了g计算在复杂的流行病学研究中的应用.