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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
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

6.9K
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...
6.9K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.3K
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...
5.3K
Systematic Sampling Method01:17

Systematic Sampling Method

10.2K
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...
10.2K
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

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相关实验视频

Updated: Jun 21, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

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在个性化随机对照 (PRACTical) 试验中确定样本大小.

Rebecca M Turner1, Kim May Lee2, A Sarah Walker1

  • 1MRC Clinical Trials Unit at University College London, London, UK.

Statistics in medicine
|July 9, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了个性化随机对照试验 (PRACTical) 中样本大小确定的新方法. 这些方法有助于确定个别患者最有效的治疗方法,减少复杂的临床环境中的不良结果.

关键词:
临床试验是指临床试验中的临床试验.多次治疗,多次治疗.个性化随机化个性化随机化样本的大小 样本大小试验设计试验设计.

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相关实验视频

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科学领域:

  • 临床试验 临床试验
  • 生物统计学 生物统计学
  • 个性化医疗是个性化的医疗.

背景情况:

  • 在复杂的临床环境中,通常缺乏标准护理,因此需要个性化治疗方法.
  • 个性化随机对照试验 (PRACTical) 只在适当的治疗中随机选择患者.
  • 现有的样本大小计算方法不适用于PRACTical设计.

研究的目的:

  • 提出用于PRACTICAL设计中确定样本大小的新方法.
  • 量化使用试验结果用于个性化治疗选择的好处.
  • 在具有多种治疗选择的环境中,为临床决策和政策制定提供信息.

主要方法:

  • 通过评估从不同规模的试验中获得的信息来推导样本大小.
  • 通过选择排名最高的治疗方法来量化减少不良结果的数量.
  • 使用模拟来评估性能指标,包括治疗有效性和患者结果.

主要成果:

  • 提出的方法为实践试验中的样本大小计算提供了一个框架.
  • 模拟证明了通过使用个性化治疗排名来减少不良结果的潜力.
  • 这种方法应用于新生儿败血症抗生素治疗方案试验 (NeoSep1).

结论:

  • 开发的方法为个性化临床试验中样本大小的确定提供了相关的方法.
  • 这有助于明智的临床决策,通过根据个体患者的适宜性对治疗进行排名.
  • 这些发现支持在非标准化护理的复杂治疗领域使用PRACTical设计.