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Related Concept Videos

Sampling Plans01:23

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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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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.
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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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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.
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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:
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Sample size considerations for stepped wedge designs with subclusters.

Kendra Davis-Plourde1,2,3, Monica Taljaard4,5, Fan Li1,3

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.

Biometrics
|October 31, 2021
PubMed
Summary
This summary is machine-generated.

New sample size methods improve stepped wedge cluster randomized trials (SW-CRTs) with subclusters. These efficient procedures accurately account for complex correlations, enhancing health services research accuracy.

Keywords:
cluster randomized trialeigenvaluesextended block exchangeable correlation structuregeneralized linear mixed modelspower analysis

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Area of Science:

  • Biostatistics
  • Health Services Research
  • Clinical Trials Methodology

Background:

  • Stepped wedge cluster randomized trials (SW-CRTs) are widely used for health intervention evaluation.
  • Accurate sample size calculation in SW-CRTs requires accounting for within-period and between-period correlations.
  • Existing methods often fail to address multiple levels of clustering, common in healthcare settings.

Purpose of the Study:

  • To develop computationally efficient sample size procedures for SW-CRTs with subclusters.
  • To differentiate and accurately model within-period and between-period intracluster correlations in complex SW-CRT designs.
  • To provide flexible methods for both Gaussian and non-Gaussian outcomes, accommodating unequal cluster sizes.

Main Methods:

  • Introduction of an extended block exchangeable correlation matrix to model complex dependencies within clusters.
  • Derivation of a closed-form sample size expression for Gaussian outcomes based on eigenvalues.
  • Development of a generic sample size algorithm for non-Gaussian outcomes using linearization and canonical link functions.

Main Results:

  • The proposed methods accurately account for multiple levels of clustering and complex correlation structures.
  • Sample size calculations for Gaussian outcomes depend on two eigenvalues of the correlation matrix.
  • For logistic models, sample size depends on three eigenvalues, with extensions for unequal cluster sizes.

Conclusions:

  • The developed methods provide accurate and efficient sample size calculations for SW-CRTs with subclusters.
  • These procedures enhance the statistical rigor of health services and policy intervention studies using SW-CRTs.
  • The methods are validated through simulations and illustrated with real-world examples.