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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

12.8K
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...
12.8K
Sample Size Calculation01:19

Sample Size Calculation

3.8K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.8K
Sampling Plans01:23

Sampling Plans

276
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
276
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
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...
3.5K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.9K
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:
5.9K

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相关实验视频

Updated: Sep 15, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

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部分集群试验的样本大小计算

Kylie M Lange1,2, Jessica Kasza3, Thomas R Sullivan1,2

  • 1School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia.

Statistics in medicine
|July 15, 2025
PubMed
概括
此摘要是机器生成的。

新的设计效应有助于确定部分聚类试验的样本大小,确保适当的统计能力. 这些方法解释了像新生儿试验这样的研究中的复杂集群.

关键词:
聚类数据是聚类数据.一般化估计方程的估计方程.权力权力权力权力权力权力权力重新随机化的设计.样本的大小 样本大小试验设计试验设计.

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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

背景情况:

  • 部分聚类试验有部分聚类和部分独立的观察结果.
  • 现有的样本大小方法对于部分聚类试验设计是有限的.

研究的目的:

  • 为了在双臂,并行,部分聚类试验中确定样本大小,呈现新的设计效应.
  • 为了解决复杂的集群试验设计的样本大小计算的局限性.

主要方法:

  • 对连续和二进制结果的设计效应的代数推导.
  • 使用通用估计方程 (GEE) 方法,具有独立性或可交换的工作相关性结构.
  • 对于集群观测,考虑了集群和个人随机化.

主要成果:

  • 设计效应取决于集群内相关性,集群大小比例,随机化方法,结果类型和相关性结构.
  • 通过模拟研究验证.
  • 为各种部分集群试验设计提供了样本大小计算的示例.

结论:

  • 新的设计效果是基于可行的参数进行试验规划.
  • 这些方法确保了部分集群试验的适当统计能力.
  • 方便在复杂的试验环境中准确确定样本大小.