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
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.7K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
5.7K
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
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.5K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.5K
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

349
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
349

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

Asymptotic validity of Schoenfeld's sample size formula for the Cox proportional hazards model via the Wald test approach.

Statistical methods in medical research·2026
Same author

A flexible dose-response modeling framework based on continuous toxicity outcomes in phase I cancer clinical trials.

Trials·2023
Same author

Clinical Outcomes in Dogs Undergoing Cholecystectomy via a Transverse Incision: A Meta-Analysis of 121 Animals Treated between 2011 and 2021.

Veterinary sciences·2023
Same author

Factors Affecting the Outcome of Medical Treatment in Cats with Obstructive Ureteral Stones Treated with Tamsulosin: 70 Cases (2018-2022).

Veterinary sciences·2022
Same author

Estimation of COVID-19 spread curves integrating global data and borrowing information.

PloS one·2020

相关实验视频

Updated: Sep 15, 2025

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

关于获取比率终点的样本大小公式的注释

Se Yoon Lee1

  • 1Department of Statistics, Texas A&M University, College Station, Texas, USA.

Statistics in medicine
|July 15, 2025
PubMed
概括

优和甘对胜率终点的样本大小公式可能低估了统计能力. 在临床试验分析中,使用近似的零方差而不是精确的顺序方差可以导致更低的功率.

科学领域:

  • 生物统计学 生物统计学
  • 临床试验设计 临床试验设计
  • 统计力量分析 统计力量分析

背景情况:

  • 胜率是一个复合终点,在临床试验中越来越多地使用.
  • 尤和甘 (YG) 公式提供了一个简化的方法来计算获胜率终点的样本大小.
  • 这个公式使用了近似的零变量,与原来的Finkelstein和Schoenfeld (FS) 精确排列变量不同.

研究的目的:

  • 为了重新评估优和甘样本大小公式的胜利比率终点.
  • 为了比较从近似的零方差公式中得出的功率与确切的变方差.
  • 在样本大小计算中确定潜在的差异及其原因.

主要方法:

  • 重新评估尤和甘的样本大小公式.
  • 使用近似的零方差与精确的变方差进行功率计算的实证比较.
  • 对场景的分析,以评估差异近似对统计功率的影响.

主要成果:

  • 使用近似零方差的样本大小公式通常与确切的变方差相比,产生较低的统计能力.
  • 功率的差异归因于在使用近似的零方差时对真方差的高估.
  • 尤和甘的公式虽然在分析上很简单,并且避免了患者层面的数据,但可能导致研究不足.
关键词:
过度估计差异的偏差.样本大小公式 样本大小公式胜利比率终点的终点是什么

更多相关视频

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

14.6K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.0K

相关实验视频

Last Updated: Sep 15, 2025

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
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

14.6K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.0K

结论:

  • 尤和甘公式中的近似零差异可能导致差异的高估,从而导致较低的统计能力.
  • 实践者应该意识到使用这种配方时可能会减少功率.
  • 需要进一步讨论在胜率分析中准确的样本大小确定公式的适当应用和潜在调整.