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

Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

610
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
610
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

60
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...
60
Survival Tree01:19

Survival Tree

61
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...
61
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

105
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
105
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

42
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
42
Improving Translational Accuracy02:07

Improving Translational Accuracy

9.2K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.2K

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

Natural History of C3 Glomerulopathy and Immune Complex-Associated Membranoproliferative Glomerulonephritis in Children.

Clinical journal of the American Society of Nephrology : CJASN·2026
Same author

Evaluation of steroids for acute COVID in the prevention of long COVID in children: An EHR and pediatric cohort study from the RECOVER Initiative.

PloS one·2026
Same author

Blood Pressure Control in Adolescents With CKD and Risk of Kidney Failure in Young Adulthood.

Kidney medicine·2026
Same author

Contemporary Treatment Responses of Recurrent Focal Segmental Glomerulosclerosis or Steroid Resistant Nephrotic Syndrome in Children after Kidney Transplantation: Phase 2 of a Multicenter Electronic Health Record Data Analysis.

Research square·2026
Same author

NIH-Funded Pediatric Research.

JAMA pediatrics·2026
Same author

Target Trial Emulation of Vaccine Effectiveness in 5- to 17-years-olds with Prior SARS-CoV-2 Infection.

Nature communications·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 8, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K

对二进制数据进行先进的可解释回归分析:一种新的分布式算法方法.

Jiayi Tong1,2, Lu Li1,3, Jenna Marie Reps4,5,6

  • 1Center for Health AI and Synthesis of Evidence (CHASE), Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Statistics in medicine
|November 3, 2024
PubMed
概括
此摘要是机器生成的。

一个新的分布式算法,ODAP-B,减少了对罕见二进制结果的相对风险估计的偏差. 这种高效的沟通方法为稀疏数据挑战提供比传统元分析更准确的结果.

关键词:
二进制数据二进制数据分布式算法是一种分布式算法.经过修改的波桑回归.相对风险是相对风险.

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

相关实验视频

Last Updated: Jun 8, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

科学领域:

  • 生物统计学 生物统计学
  • 流行病学 流行病学
  • 分布式学习 分布式学习

背景情况:

  • 稀少的数据偏差是分析罕见的二进制结果的一个重大挑战.
  • 现有的两步元分析方法可以减少但不能消除对效果估计的偏差.

研究的目的:

  • 提出一种新型的一次性分布式算法,ODAP-B,用于对二进制数据分析中的公正的相对风险估计.
  • 用模拟和现实世界的数据来评估ODAP-B与传统元分析的性能.

主要方法:

  • 在分布式学习框架内,ODAP-B对二进制数据采用了修改后的Poisson回归.
  • 该算法是有效的通信和保护隐私,利用聚合数据.
  • 为了可靠的推断,一个强大的方差估计器被纳入.

主要成果:

  • 与两步元分析方法相比,ODAP-B在各种结果中提供了更准确的相对风险估计.
  • 模拟和案例研究,包括在儿童中SARS-CoV-2感染的急性后续症状,证明了ODAP-B的有效性.
  • 这种方法在缓解稀疏数据偏差,以减少罕见的二进制结果方面被证明是优越的.

结论:

  • ODAP-B是一种有效的Poisson回归分布式学习算法,特别适用于罕见的二进制结果.
  • 该算法提供了一种有效的通信和保护隐私的解决方案,用于公正的效应估计.
  • ODAP-B 增强了流行病学和生物统计学研究中稀疏数据集的分析.