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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
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.7K
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:
5.7K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.1K
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...
3.1K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.2K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.2K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

168
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
168
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K

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相关实验视频

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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样本大小和功率计算与基于层次终点的Win测量

Huiman Barnhart1,2, Yuliya Lokhnygina1,2, Roland Matsouaka1,2

  • 1Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, USA.

Statistics in medicine
|May 19, 2025
PubMed
概括

新公式简化了临床试验的样本大小和功率计算,使用win措施分析等级终点. 这种方法减少了对复杂的模拟和难以获得的数据的依赖,以便准确的研究规划.

关键词:
门 门 是一个门.层次的终点是指等级的终点.净收益 净收益 净收益 净收益样本的大小和功率.赢得几率几率几率.赢得比率比率的胜利比率.

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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
  • 统计方法 统计方法

背景情况:

  • 胜利指标 (胜利比率,胜利几率,净效益,DOOR) 越来越多地用于临床研究中的等级终点.
  • 当前的样本大小和功率计算通常依赖于繁的模拟或难以引起的参数.
  • 从现有文献或初步数据来确定研究人员指定的临床意义上的胜利指标和平局概率是具有挑战性的.

研究的目的:

  • 为四个共同的win措施开发新的样本大小和功率计算公式.
  • 提供从易于获得的边际规范中计算整体胜利指标和平局概率的方法.
  • 为了使对等级终点的样本大小和功率估计更容易获得和合理.

主要方法:

  • 对四个胜率测量的样本大小和功率计算公式的推导.
  • 开发公式来将边际胜利措施和绑定概率转化为整体措施.
  • 进行广泛的模拟研究以验证衍生式的准确性.

主要成果:

  • 拟议的公式为各种相关的等级终点提供了与模拟结果可比的准确功率估计.
  • 该方法允许基于端点的数量,排序和类型来评估功率.
  • 公式在不同类型的等级终点上是有效的,在非常高的相关性场景中存在轻微的差异.

结论:

  • 开发的公式提供了一个实用和强大的替代方案,以模拟为基础的计算在等级终点分析中赢得措施.
  • 这些公式有助于有意义和合理的规范赢得措施和平局概率.
  • 该方法提高了临床试验设计中的样本大小和功率计算的效率和准确性.