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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

129
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
129
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

41
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
41
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

127
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
127
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

430
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...
430
Confidence Intervals01:21

Confidence Intervals

6.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.2K

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

Toward Evidence Synthesis of Adverse Events in Imbalanced Time-to-Event Data.

Journal of evidence-based medicine·2026
Same author

Gut microbial bile salt hydrolase as a metabolic gatekeeper in digestive homeostasis and disease.

Frontiers in immunology·2026
Same author

Trial-design-aware funnel plot for publication bias assessment with non-inferiority or equivalence objectives.

Journal of clinical epidemiology·2026
Same author

Ensitrelvir for the treatment of hospitalized adults with COVID-19: an international phase 3 randomized placebo-controlled trial.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Unpublished trials affected evidence synthesis substantially when estimating medication harms in children.

Journal of clinical epidemiology·2026
Same author

The hazards of using hazard ratios from proportional hazard models in indirect treatment comparisons.

Research synthesis methods·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 2, 2025

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

512

贝叶斯的非参数元分析模型用于估计参考区间.

Wenhao Cao1, Haitao Chu1,2, Timothy Hanson3

  • 1Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA.

Statistics in medicine
|February 27, 2024
PubMed
概括
此摘要是机器生成的。

建立准确的参考间隔对于健康诊断至关重要. 这项研究引入了一种灵活的贝叶斯方法来进行元分析,提高了传统方法之外的概括性.

关键词:
贝叶斯的非参数的贝叶斯式.这是一个元分析.规范范围的范围是规范范围.随机效应是一种随机效应.参考时间间隔的参考时间间隔.

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
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

2.1K

相关实验视频

Last Updated: Jul 2, 2025

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

512
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
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

2.1K

科学领域:

  • 生物统计学 生物统计学
  • 临床实验室科学 临床实验室科学
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 参考间隔定义了健康人口规范,对于临床实验室检测和疾病分化至关重要.
  • 单一研究的参考间隔缺乏广泛的适用性;元分析提供了概括性,但依赖于限制性假设.
  • 对于参考区间的现有元分析方法通常假定正常分布的研究平均值和相同的差异,这可能不反映真实世界的数据.

研究的目的:

  • 开发一个更强大的统计模型来估计可概括的参考区间.
  • 通过使用灵活的假设来克服现有的元分析技术的局限性.
  • 提高临床实验室测试中参考间隔的准确性和适用性.

主要方法:

  • 为随机效应的元分析提出了贝叶斯的非参数模型.
  • 该模型包含了更灵活的假设,涉及研究特定的平均值和研究内部差异.
  • 通过模拟研究和真实世界的临床数据来评估模型的性能.

主要成果:

  • 建议的贝叶斯非参数模型在违反标准假设时,与传统方法相比,表现优越.
  • 该方法有效地估计了参考间隔,即使研究平均值没有正常分布或差异不均.
  • 模拟和真实数据分析证实了模型的稳定性和通用性.

结论:

  • 新的贝叶斯非参数方法为估计可概括的参考区间提供了更强大的方法.
  • 当研究水平的数据与传统的元分析假设有所不同时,这种方法特别有价值.
  • 通过提供更准确和广泛适用的参考范围,提高了实验室测试的可靠性.