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相关概念视频

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...
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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
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
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
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
Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
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...
11.6K

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相关实验视频

Updated: May 31, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
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Measuring Delay Discounting in Humans Using an Adjusting Amount Task

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序列多重分配随机试验中的样本大小调整

Liwen Wu1, Junyao Wang1, Abdus S Wahed2

  • 1Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, MA.

Statistics in medicine
|January 24, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了顺序多重分配试验 (SMARTs) 的样本大小调整方法. 该程序通过在中间分析中重新计算样本大小来确保足够的统计能力,从而优化临床试验的效率.

关键词:
智能设计是智能设计.适应性治疗策略 适应性治疗策略动态处理制度 动态处理制度间期监测 间期监测 间期监测 间期监测调整样本大小的调整.顺序的多重分配随机试验随机试验.

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相关实验视频

Last Updated: May 31, 2025

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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:

  • 临床试验经常面临由于初始参数不确定性的不足.
  • 顺序多重分配试验 (SMARTs) 对样本大小的确定提出了独特的挑战.
  • 现有的样本大小调整方法不适用于SMARTs.

研究的目的:

  • 专门为SMARTs提出一种新的样本大小调整程序.
  • 为了确保SMART的足够的统计能力,尽管最初的设计参数限制.
  • 在临床试验中优化资源配置,只投资那些具有有前途的条件功率的人.

主要方法:

  • 基于条件功率的样本大小调整程序是为SMARTs开发的.
  • 条件功率来自于一个双变的非中心的奇方分布.
  • 临时分析用于重新估计样本大小并调整试验设计.

主要成果:

  • 提出的方法有效地保持了可取的统计能力,即使初始样本大小不足.
  • 模拟研究证实了该程序在最终分析中保持功率的能力.
  • 该方法允许有效地分配资源,将额外的投资集中在具有证明潜力的试验上.

结论:

  • 开发的样本大小调整方法提高了SMARTs的稳定性.
  • 该程序解决了复杂治疗策略的适应性试验设计中的关键缺口.
  • 该方法为提高临床试验的效率和成功率提供了一个实际的解决方案.