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相关概念视频

Sample Size Calculation01:19

Sample Size Calculation

3.2K
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...
3.2K
Group Design02:01

Group Design

8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K
Randomized Experiments01:13

Randomized Experiments

6.7K
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...
6.7K
Random Sampling Method01:09

Random Sampling Method

11.0K
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. Data are the result of sampling from a 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. Among the various sampling methods used by...
11.0K
Systematic Sampling Method01:17

Systematic Sampling Method

10.0K
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. Data are the result of sampling from a 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.
Systematic sampling is one of the simplest methods...
10.0K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.2K
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...
3.2K

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相关实验视频

Updated: May 31, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models
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Barnes Maze Testing Strategies with Small and Large Rodent Models

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贝叶斯样本大小计算在小n,顺序多重分配随机试验 (snSMART) 中.

Fang Fang1, Roy N Tamura2, Thomas M Braun1

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

Pharmaceutical statistics
|January 23, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了两种新的方法来确定小n,顺序,多重分配,随机试验 (snSMART) 的样本大小,比较两个剂量与安慰剂. 这两种方法都有效地确保了临床试验效率所需的统计能力.

关键词:
贝叶斯统计学 贝叶斯统计学临床试验临床试验临床试验临床试验临床试验罕见疾病是一种罕见的疾病.反复采取措施反复采取措施.

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相关实验视频

Last Updated: May 31, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models
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Published on: February 26, 2014

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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:

  • 小样本规模的临床试验需要高效的设计.
  • 小n,顺序,多重分配,随机试验 (snSMART) 是适应这种设置的适应性设计.
  • 之前的研究提出了贝叶斯的方法来估计snSMART的疗效.

研究的目的:

  • 为snSMART设计提出和评估两种新的样本大小确定 (SSD) 方法.
  • 在snSMART框架内,比较两个剂量水平与安慰剂.
  • 确保在小型试验中确保治疗效果估计的足够的统计能力.

主要方法:

  • 开发了基于平均覆盖标准 (ACC) 的两个样本大小确定 (SSD) 方法.
  • 方法1:使用后方方差的单步计算.
  • 方法2:为单阶段设计采用两步方法,使用调整因子 (AF).
  • 通过模拟研究验证的方法.

主要成果:

  • 两种建议的SSD方法都成功地实现了所需的统计功率.
  • 通过新方法计算的样本大小适合于snSMART试验.
  • 模拟证实了样本大小计算方法的可靠性.

结论:

  • 建议的样本大小确定方法对于snSMART试验是有效的.
  • 这些方法提高了用小样本大小进行临床试验的效率.
  • 一个附带的小程序方便了这些SSD技术的实际应用.