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

Sample Size Calculation01:19

Sample Size Calculation

3.8K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.8K
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
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Margin of Error01:27

Margin of Error

4.5K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
4.5K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

337
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
337
Survival Tree01:19

Survival Tree

166
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...
166

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

Author Correction: The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence.

Nature medicine·2026
Same author

What influences whether researchers adhere to healthcare reporting guidelines successfully? A thematic synthesis.

Research integrity and peer review·2026
Same author

A scoping review of guidance on sharing trial results with patients and the public finds inconsistent recommendations and limited methodological development.

Journal of clinical epidemiology·2026
Same author

Stakeholder perspectives on potential open-label bias in patient-reported outcome results from cancer clinical trials: evidence from the IMPROvE project.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2026
Same author

SPIRIT 2025 statement: updated guideline for protocols of randomised trials.

Lancet (London, England)·2026
Same author

Recommendations for studying the safety, efficacy and durability of intracranial aneurysm devices.

European stroke journal·2026

相关实验视频

Updated: Sep 17, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.3K

扩展样本大小计算用于评估使用分类值的预测模型.

Rebecca Whittle1,2, Joie Ensor3,4, Lucinda Archer3,4

  • 1Department of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK. r.l.whittle@bham.ac.uk.

BMC medical research methodology
|July 2, 2025
PubMed
概括

计算适当的样本大小对于准确的个性化风险预测模型至关重要. 这项研究提供了新的公式和代码来确定最低样本大小,以精确估计基于值的绩效指标,增强模型评估.

关键词:
分类模型的分类模型.临床预测模型的临床预测模型.外部验证的验证方法模型评价模型评价绩效指标是指性能指标.样本的大小 样本大小这是一个值.

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

2.2K
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

7.6K

相关实验视频

Last Updated: Sep 17, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.3K
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

2.2K
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

7.6K

科学领域:

  • 生物统计学 生物统计学
  • 医疗信息学 医疗信息学
  • 流行病学 流行病学

背景情况:

  • 准确的样本大小计算对于可靠的个性化风险预测模型至关重要.
  • 现有的指导侧重于校准,歧视和净收益,但往往忽视了基于门的措施.
  • 在临床实践中,基于值的绩效指标经常被报告.

研究的目的:

  • 为预测模型扩展现有的样本大小指导.
  • 为准确估计基于值的绩效指标提供方法.
  • 为在外部验证研究中计算最低样本大小提供工具.

主要方法:

  • 开发了闭式解决方案,用于精确度,特异性,灵敏度,正预测值 (PPV) 和负预测值 (NPV) 的样本大小估计.
  • 用一种代方法来估计F1得分的样本大小.
  • 包含用户定义的目标标准误差,预期性能值和结果流行率.
  • 考虑扩展时间到事件的结果.

主要成果:

  • 为样本大小计算提供了新的公式和计算工具 (Python,R,Stata).
  • 在示例中,基于值的测量所需的样本大小往往小于校准斜率估计.
  • 这些方法可以在外部验证中精确估计各种绩效指标.

结论:

  • 开发的方法和工具有助于准确地确定以值为基础的绩效指标的样本大小.
  • 这些标准应补充现有的指南,以全面评估预测模型.
  • 研究人员现在可以更可靠地评估强大的模型验证所需的最小样本大小.