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相关概念视频

One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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Significance Testing: Overview01:04

Significance Testing: Overview

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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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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...
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Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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Updated: Jun 23, 2025

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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多变量测试和效果大小测量用于对放射性特征进行批量效应评估.

Hannah Horng1,2,3, Christopher Scott4, Stacey Winham4

  • 1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA. hannah.horng@gmail.com.

Scientific reports
|June 17, 2024
PubMed
概括
此摘要是机器生成的。

新的统计方法PERMANOVA和RESI改善了放射学数据中批量效应的检测和量化,提高了精准医学应用的可重复性.

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科学领域:

  • 医学成像分析 医学成像分析
  • 生物统计学 生物统计学
  • 无线电学 (Radiomics) 是一种放射学.

背景情况:

  • 放射学分析对精密医学具有前景,但图像采集的变性引入了损害可重现性的批量效应.
  • 目前在放射学中评估批量效应的方法不一致,阻碍了可靠的下游预测分析.

研究的目的:

  • 引入和评估PERMANOVA和RESI作为强大的统计工具,用于量化放射学数据中的批量效应.
  • 为了比较PERMANOVA和RESI的性能与标准单变量统计测试进行批量效应评估.

主要方法:

  • 使用了多变量统计测试PERMANOVA和强大的效应大小指数 (RESI).
  • 使用模拟放射性特征和真实放射性特征从全场数字造乳镜 (FFDM) 数据评估的方法.
  • 我们比较了PERMANOVA对单变量统计测试的强度和RESI对大样本大小的解释性.

主要成果:

  • 与检测批量效应的标准单变量测试相比,PERMANOVA显示出更高的统计能力.
  • RESI有效量化了特定地点变化的效果大小,即使使用非常大的数据集.
  • 这两种方法在放射学特征中的批量效应的表征方面都非常有价值.

结论:

  • 在放射学研究中,PERMANOVA 和 RESI 提供了更强大,更易于解释的方法来检测和量化批量效应.
  • 这些方法可以提高精准医学放射学分析的可复制性和可靠性.
  • 加强批量效应评估对于推进放射学临床应用至关重要.