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相关概念视频

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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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 Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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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...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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具有多个测试源的测试负面设计.

Mengxin Yu1, Nicholas P Jewell2

  • 1Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.

medRxiv : the preprint server for health sciences
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概括
此摘要是机器生成的。

测试阴性设计 (TND) 对于评估传染病疫苗至关重要. 这项研究通过分析症状和无症状病例来解决TND的偏差,并提出一种估计埃博拉疫苗疗效的方法.

关键词:
一个案例-队列研究.埃博拉病毒埃博拉病毒埃博拉病毒测试负面的设计.

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科学领域:

  • 流行病学 流行病学
  • 疫苗学 疫苗学 疫苗学
  • 生物统计学 生物统计学

背景情况:

  • 测试阴性设计 (TND) 广泛用于传染病干预评估,包括流感和COVID-19的疫苗.
  • 传统的TND依赖于因症状而被测试的个体,减轻了寻求医疗保健的行为偏见.
  • 最近的应用,比如COVID-19和埃博拉,涉及各种原因 (例如,联系人追踪) 的测试,在汇总结果时可能引入偏见.

研究的目的:

  • 解决TND中"测试多种原因"的问题.
  • 提出一种方法来估计疫苗的疗效,使用症状和无症状的测试结果.
  • 在埃博拉疫苗试验中,评估疫苗疗效是否在症状和无症状个体之间存在差异.

主要方法:

  • 使用了经过修改的阴性测试设计,包括出现症状的患者需要护理,以及已确诊病例的无症状密切接触者.
  • 从这两个不同的测试来源开发了一个统计方法来估计一个共同的疫苗疗效.
  • 在症状和无症状的参与者群体中评估了疫苗有效性的一致性.

主要成果:

  • 该研究检查了埃博拉疫苗试验的特定测试负面设计场景.
  • 提出了一种方法,通过结合症状和无症状个体的数据来估计疫苗的疗效.
  • 分析包括评估估计的疗效是否在两组之间有所不同.

结论:

  • 在TND中,症状和无症状测试结果的聚合可能导致偏差的疗效估计.
  • 拟议的方法提供了一种在复杂的测试场景中估计疫苗疗效的方法.
  • 需要进一步评估,以了解疫苗的疗效是否在不同的测试指示中是一致的.