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相关概念视频

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
Data Collection by Experiments01:13

Data Collection by Experiments

25.2K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
25.2K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.6K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
5.6K
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
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

174
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,...
174
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 10, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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在使用治疗效果尺度时,先前的有效样本大小

Hongtao Zhang1, Keaven M Anderson1, Zachary Zimmer1

  • 1Biostatistics and Research Decision Sciences, Merck & Co. Inc., North Wales, PA, USA.

Statistics in medicine
|August 22, 2025
PubMed
概括

之前的有效样本大小 (ESS) 对贝叶斯式外部数据借用至关重要. 这项研究将预期的局部信息比率 (ELIR) ESS定义扩展到治疗效果尺度,解决了改善试验设计的关键方法差距.

关键词:
贝叶斯方法外部数据小儿外推之前的分发

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科学领域:

  • 生物统计学
  • 临床试验
  • 贝叶斯推理

背景情况:

  • 在临床试验中越来越多地使用贝叶斯式外部数据借用.
  • 精确的先前有效样本大小 (ESS) 对于控制借用信息至关重要.
  • 现有的ESS方法主要集中在借款控制上,而不是治疗效果尺度.

研究的目的:

  • 将预期的局部信息比率 (ELIR) 的ESS定义扩展到治疗效果尺度.
  • 为各种终点和治疗效果测量提供一个一般框架并推导ESS.
  • 评估拟议的ELIR ESS的预测一致性特性.

主要方法:

  • 扩大预期的本地信息比率 (ELIR) 的ESS定义.
  • 对各种终点类型和治疗效果的ESS导出.
  • 对不同终点和治疗效果组合的预测一致性的评估.

主要成果:

  • 已经成功地将ELIR ESS定义扩展到治疗效果尺度.
  • 针对多个终点和治疗效果类型,先前的ESS公式得到了推导.
  • 只有两个正常终点之间的差异保持了预测一致性.

结论:

  • 开发的方法解决了在治疗效果尺度上计算先前的ESS的差距.
  • 这些发现强调了在应用ELIR ESS时考虑终点和治疗效应类型的重要性.
  • 已有R实施方案可用于在实践中促进这些新型ESS方法的应用.