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相关概念视频

Sample Size Calculation01:19

Sample Size Calculation

3.3K
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...
3.3K
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
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.3K
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...
3.3K
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
Sampling Plans01:23

Sampling Plans

181
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...
181
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

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相关实验视频

Updated: Jun 27, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

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在两阶段无设计中进行最佳样本大小划分.

Lindsay R Berry1, Joe Marion1, Scott M Berry1,2

  • 1Berry Consultants, LLC, Austin, Texas, USA.

Pharmaceutical statistics
|April 27, 2024
PubMed
概括

与单独的试验相比,无的2/3期临床试验设计提供了更大的功率和稳定性. 最优的无设计在各种响应场景中更容易适应,为实施提供了实际指导.

科学领域:

  • 临床试验方法论 临床试验方法论
  • 生物统计学 生物统计学
  • 药物开发 药物开发

背景情况:

  • 无二/三期设计在临床研究中越来越受欢迎.
  • 了解它们在单独的第二阶段和第三阶段试验中的益处至关重要.
  • 设计选择,比如将患者分配到第二阶段,会影响试验结果.

研究的目的:

  • 为了比较无2/3期设计与单独的2期和3期试验的性能.
  • 评估不同患者比例在无设计的第二阶段部分的影响.
  • 确定临床试验的最佳设计策略.

主要方法:

  • 在多个试验臂和疗效响应曲线上进行了一项模拟研究.
  • 该研究将单独的试验设计与无的2/3阶段设计进行了比较.
  • 第二阶段的患者分配比例 (0%-100%) 系统地变化.

主要成果:

  • 与单独的试验设计相比,无设计显示出更高的统计能力.
  • 最佳无设计在各种响应场景中表现出更大的稳定性.
  • 在无试验中,2期患者分配范围为30%-50%几乎是最佳的.
  • 单独的试验设计显示出较少的适应性,最佳百分比在不同情景中差异很大.
关键词:
多次测试多次测试多次测试无的2/3阶段试验治疗选择,治疗选择.

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结论:

  • 在运行和科学上可行时,无的2/3阶段试验提供了卓越的性能和稳定性.
  • 这些发现为在无试验设计中选择最佳患者分配提供了实际指导.
  • 无设计代表了对传统单独的第二阶段和第三阶段试验的高效进步.