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

One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K
Study Design in Statistics01:15

Study Design in Statistics

8.0K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
8.0K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

251
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:  
251
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

164
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
164
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

Real-world performance of large-scale propensity score adjustment strategies: Matching, weighting, and stratification.

Research square·2026
Same author

Superior hypogastric plexus block - Transdiscal approach.

Interventional pain medicine·2026
Same author

A comparison of Fast Healthcare Interoperability Resources and Observational Medical Mutcomes Partnership electronic health record data within the All of Us Research Program.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Reconciling fast Hepatitis B evolutionary rates with ancient co-divergence.

bioRxiv : the preprint server for biology·2026
Same author

Navigating the landscape of novel meshes: comprehensive review of physicomechanical categorization of mesh for hernia repair.

Hernia : the journal of hernias and abdominal wall surgery·2026
Same author

Phenome-wide analysis of downstream health outcomes following second-line antidiabetic agent prescriptions in All of Us.

Nature communications·2026

相关实验视频

Updated: Jun 27, 2025

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

用小样本大小评估共变平衡的方法

George Hripcsak1,2,3, Linying Zhang2,4, Yong Chen2,5,6,7

  • 1Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.

medRxiv : the preprint server for health sciences
|May 7, 2024
PubMed
概括
此摘要是机器生成的。

倾向性得分调整诊断可以错误地标记由于偶然而导致的不平衡,特别是在元分析中. 一种新的诊断方法通过测试统计学上显著的不平衡来提高准确性,提高研究有效性.

关键词:
混是一种混.共同变量平衡 共同变量平衡这是一个元分析.观察性研究是观察性研究.倾向性得分是指倾向性得分.

更多相关视频

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.9K
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

2.0K

相关实验视频

Last Updated: Jun 27, 2025

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
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.9K
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

2.0K

科学领域:

  • 生物统计学 生物统计学
  • 流行病学 流行病学
  • 健康研究方法 健康研究方法

背景情况:

  • 倾向性得分调整方法 (匹配,分层,加权) 用于控制观察性研究中的混.
  • 标准诊断,像标准化平均差异 (SMD) 值一样,评估共变量平衡,但在小到中等样本大小中可能不可靠.
  • 机会失衡可能导致错误地拒绝研究的有效性,特别是在使用SMD的固定值时.

研究的目的:

  • 为解决因机会失衡而导致的倾向性得分诊断中I型错误率膨胀的问题.
  • 提出和评估一种用于调整倾向得分的新型诊断方法,以增加元分析的精度.
  • 在大规模研究和元分析中提高协变量平衡评估的可靠性和严格性.

主要方法:

  • 提出了一种替代诊断方法,通过统计测试是否标准化平均差异 (SMD) 显著超过预定义的值.
  • 通过使用模拟和现实世界的数据,对标准名义值测试对拟议诊断的性能进行了评估.
  • 研究了诊断在元分析中的行为,强调了对诊断的元分析以及效果估计的需要.

主要成果:

  • 机会失衡在现实环境中是一个重要的问题,即使样本大小高达2000年.
  • 提出的诊断表明,在各种样本大小 (250-4000) 和共变量数量 (20-100,000) 中,I型错误率和统计能力之间的优异权衡.
  • 分析需要伴随着诊断的分析,以防止系统的混从压倒性的效果估计.

结论:

  • 拟议的统计学显著值诊断为评估倾向性得分调整中的共变量平衡提供了更强大的方法.
  • 这种方法对于确保元分析结果的有效性至关重要,特别是在网络研究中,混可能很大.
  • 该程序促进了对众多共变量的审查,从而导致更严格和可靠的研究诊断.