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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...
12.0K
Sampling Plans01:23

Sampling Plans

208
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...
208
Randomized Experiments01:13

Randomized Experiments

7.0K
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...
7.0K
Sample Size Calculation01:19

Sample Size Calculation

3.4K
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...
3.4K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
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:
5.8K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

147
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
147

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相关实验视频

Updated: Jul 16, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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外部试点集群随机试验的样本大小确定与二元可行性结果:一个教程.

K Hemming1, M Taljaard2,3, E Gkini4

  • 1Institute of Applied Health Research, University of Birmingham, Birmingham, UK. k.hemming@bham.ac.uk.

Pilot and feasibility studies
|September 19, 2023
PubMed
概括
此摘要是机器生成的。

为了证明试点试验的样本大小,需要特定的方法,而不是标准的有效性测试. 本研究为试点集群试验提供了指导,重点关注可行性结果,并为研究人员提供实用工具.

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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
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 试点试验需要证明样本大小的合理性,但目前的方法往往被错误地应用.
  • 传统的样本大小方法不适合试点试验,试点试验侧重于可行性,而不是有效性.
  • 可行性目标,通常是二进制的,为随后的全面试验提供样本大小的信息.

研究的目的:

  • 为了证明外部试点集群试验的样本大小合理性,估计二元可行性结果.
  • 在这种情况下,为样本大小计算提供实用工具和公式.
  • 报告可行性结果的集群内部相关系数.

主要方法:

  • 在试点集群试验中为各种场景开发样本大小计算公式.
  • 创建一个R Shiny应用程序来实现拟议的样本大小方法.
  • 从现有数据中编制可行性结果的集群内部相关系数.

主要成果:

  • 试点集群试验的样本大小计算取决于集群的数量,集群大小和可行性结果的集群内相关系数 (ICC).
  • 可行性结果可能显示出比临床结果更大的ICC,现有数据有限.
  • 外部试点集群试验的效率可以通过增加集群数量和减少每个集群的观测量来提高,除非ICC非常低.

结论:

  • 本教程提供了一个框架,用于证明试点集群试验中的样本大小,重点关注二进制可行性结果.
  • 提供的R Shiny应用程序有助于这些样本大小方法的实际应用.
  • 了解可行性结果的ICC对于优化试点集群试验设计和效率至关重要.