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

Cochran's Q Test01:17

Cochran's Q Test

242
Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square...
242
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

158
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...
158
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

112
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
112
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

1.6K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
1.6K
Behrens–Fisher Test00:57

Behrens–Fisher Test

69
The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
69
Statistical Significance01:50

Statistical Significance

20.1K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
20.1K

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

Group-Sequential Designs With an Externally-Driven Change of Primary Endpoint.

Statistics in medicine·2025
Same author

Statistical methods for clinical trials interrupted by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic: A review.

Statistical methods in medical research·2024
Same author

Group sequential designs for clinical trials when the maximum sample size is uncertain.

Statistics in medicine·2024
Same author

Evidence-informed recommendations on managing breast screening atypia: perspectives from an expert panel consensus meeting reviewing results from the Sloane atypia project.

The British journal of radiology·2024
Same author

Should the two-trial paradigm still be the gold standard in drug assessment?

Pharmaceutical statistics·2022
Same author

The Value of the Information That Can Be Generated: Optimizing Study Design to Enable the Study of Treatments Addressing an Unmet Need for Rare Pathogens.

Open forum infectious diseases·2022

相关实验视频

Updated: Jun 12, 2025

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:27

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

686

在选定的子组中测试治疗效果.

Nigel Stallard1

  • 1Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK.

Statistical methods in medical research
|September 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究为分析基于生物标志物的治疗效果的临床试验引入了统计框架. 它提供了两种测试,以控制在基于连续生物标志物的特定患者亚组中识别治疗益处时的错误.

关键词:
适应性丰富设计的设计.家庭明智的错误率控制测试的层次结构测试.线性回归是一种线性回归.小组选择选择子组的选择.

更多相关视频

Strategies for Assessing Autistic-Like Behaviors in Mice
07:38

Strategies for Assessing Autistic-Like Behaviors in Mice

Published on: September 20, 2024

898
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

451

相关实验视频

Last Updated: Jun 12, 2025

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:27

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

686
Strategies for Assessing Autistic-Like Behaviors in Mice
07:38

Strategies for Assessing Autistic-Like Behaviors in Mice

Published on: September 20, 2024

898
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

451

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
  • 药物基因组学 药物基因组学

背景情况:

  • 越来越多的人对使用连续生物标志物来预测治疗反应的个性化医学感兴趣.
  • 当使用相同的数据进行值选择和子组分析时,会出现统计方面的挑战.
  • 需要强大的方法来控制生物标志物引导的临床试验中的I型错误率.

研究的目的:

  • 提出一个层次化的测试框架,用于家庭的I型错误率控制.
  • 引入两种新的统计测试,用于确定生物标志物定义的子群体中的治疗效应.
  • 为了解决临床试验中基于值的子组分析的统计复杂性.

主要方法:

  • 开发一个层次化的测试程序.
  • 建议进行两种不同的统计测试:一种基于线性回归与相互作用,另一种更强大的替代方案.
  • 在不同的假设下对家族类型I错误率控制的评估.

主要成果:

  • 拟议的框架提供了对家庭类型I错误率的控制.
  • 基于线性回归的测试很强大,但对违反模型假设很敏感.
  • 当线性模型假设不满足时,更强大的测试提供了更好的性能,尽管功率略有降低.

结论:

  • 层次测试框架在生物标志物分层治疗效果分析中有效地管理I型错误.
  • 两种拟议的试验之间的选择取决于具体的临床试验背景和数据特征.
  • 这些方法提高了基于连续生物标志物的目标患者群体中识别治疗益处的统计学严谨性.