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Updated: May 31, 2025

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
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生物等价性设计与采样分布段

Luke Hagar1, Nathaniel T Stevens2

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.

Statistics in medicine
|January 24, 2025
PubMed
概括

本研究引入了一种更快的仿真方法,用于生物等效功率分析,为临床试验设计提供准确的样本大小建议. 该方法有效地近似功率曲线,而不需要完全估计分布.

关键词:
这就是Sobol'序列.韦尔奇的t-试验平均生物等价性的平均值.动力分析分析能力分析可扩展的设计可扩展的设计.

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Sampling Plans01:23

Sampling Plans

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...
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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Stratified Sampling Method

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.
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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