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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Correlation of Experimental Data01:23

Correlation of Experimental Data

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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
253
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

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The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

222
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...
222
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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Coefficient of Correlation01:12

Coefficient of Correlation

6.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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相关实验视频

Updated: Jul 19, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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它会腐烂吗?没有. 从先前分析的集群随机试验中获得衰减相关性参数值.

Jessica Kasza1, Rhys Bowden1, Yongdong Ouyang2,3

  • 1School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.

Statistical methods in medical research
|August 17, 2023
PubMed
概括
此摘要是机器生成的。

集群随机试验通常假设相等的集群内相关性. 这项研究表明如何使用现有的相关性估计来建模衰变的相关性,提高研究功率和准确性.

关键词:
集群自相关性 集群自相关性层次结构模型的模型.集群内部相关性相关性样本大小计算的计算一步一步的形.集群内部的相关性结构.

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科学领域:

  • 生物统计学 生物统计学
  • 临床试验 临床试验
  • 流行病学 流行病学

背景情况:

  • 集群随机试验 (CRT) 通常假定集群内的所有参与者对具有恒定的集群内相关性 (ICC).
  • 如果测量之间的相关性随着时间的推移而衰减,这个假设可能会被违反.
  • 忽视相关性衰变可能导致研究不足,治疗效果的置信区间不准确.

研究的目的:

  • 为了证明如何从现有的ICC估计得出可信的衰减相关结构.
  • 为计划CRT的研究人员提供指导,特别是在怀疑但没有建模的相关性衰变时.
  • 开发实用工具来评估衰变相关性对样本大小和功率计算的影响.

主要方法:

  • 从具有可交换或可块交换结构的线性混合模型中使用的集群内相关性 (ICC) 估计.
  • 开发了从标准ICC估计中推断衰变相关性值的方法,这些估计忽略了衰变.
  • 提交了一份在线申请,以促进对临床试验规划中衰减相关性的估计.

主要成果:

  • 表明,假设没有衰变的模型中的ICC值可以告知可信的衰变相关结构.
  • 展示了一种方法,从标准ICC估计中获得衰减相关性的估计.
  • 研究人员可以使用一个可访问的在线工具来探索衰减相关性的影响.

结论:

  • 从标准ICC估计值中可以推导出有意义的衰减相关性参数.
  • 这种方法可以在CRT中进行更准确的样本大小和功率计算,其中相关性衰减是一个问题.
  • 开发的方法和工具有助于通过考虑时间相关性衰减来进行可靠的试验设计和分析.