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相关概念视频

Randomized Experiments01:13

Randomized Experiments

6.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Bonferroni Test01:10

Bonferroni Test

2.7K
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...
2.7K
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

120
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
120
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

186
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
186
Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.0K
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

120
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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相关实验视频

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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脑卒中和虚弱指数:一个双样本的门德尔随机化研究.

Jiangnan Wei1, Jiaxian Wang1, Jiayin Chen1

  • 1Department of Nursing, Zhuhai Campus of Zunyi Medical University, No. 368 Jinwan Road, Zhuhai, Guangdong, China.

Aging clinical and experimental research
|May 22, 2024
PubMed
概括

这项研究发现中风和虚弱指数 (FI) 之间存在因果关系,这表明遗传因素导致中风患者的虚弱. 这些发现支持中风和脆弱性之间的遗传联系.

关键词:
脆弱性 脆弱性 脆弱性基因分析 基因分析门德尔的随机化一次性中风,中风.

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科学领域:

  • 遗传学 是一个遗传学.
  • 神经学 神经学
  • 老年学是一门学科.

背景情况:

  • 观察性研究表明,中风患者的虚弱风险增加.
  • 关于中风和虚弱之间的因果关系的证据仍然有限.
  • 这项研究旨在确定中风和脆弱指数 (FI) 之间的因果关系.

研究的目的:

  • 使用遗传数据调查中风和脆弱指数 (FI) 之间的潜在因果关系.
  • 为了利用孟德尔的随机化推断中风和虚弱之间的因果关系.

主要方法:

  • 对中风的全基因组关联研究 (GWAS) 数据 (MEGASTROKE联盟) 和脆弱指数 (英国BioBank,瑞典TwinGene) 进行了聚合.
  • 两个样本的门德尔随机化 (MR) 分析使用逆方差加权 (IVW),加权中位数和其他方法进行.
  • 使用科克兰的Q测试和MR-Egger回归,评估了异质性和水平向性.

主要成果:

  • 在中风遗传预测和FI (OR 1.104,P < 0.001) 之间观察到显著的因果关系.
  • 在缺血性中风 (IS) (OR 1.037,P = 0.005) 和大血管动脉样性中风 (LAS) (OR 1.081,P < 0.001) 中发现了显著的因果关系.
  • 没有发现心血管栓塞性中风 (CES) 和FI之间的因果关系;没有发现显著的异质性或性.

结论:

  • 这项门德尔随机化研究提供了强有力的证据,证明中风和脆弱指数之间存在因果关系.
  • 这些发现提供了关于中风背景下脆弱性遗传基础的新见解.
  • 对中风的遗传倾向可能有助于脆弱的发展.