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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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...
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...
Sample Size Calculation01:19

Sample Size Calculation

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...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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...
Randomized Experiments01:13

Randomized Experiments

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...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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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Relative efficiency and sample size for cluster randomized trials with variable cluster sizes.

Zhiying You1, O Dale Williams, Inmaculada Aban

  • 1Department of Medicine, School of Medicine, University of Alabama, Birmingham, AL, USA. zyou@mail.dopm.uab.edu

Clinical Trials (London, England)
|December 18, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to calculate sample size for cluster randomized trials with varying cluster sizes. It uses noncentrality parameters to determine relative efficiency, improving power calculations.

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Power Analysis

Background:

  • Cluster randomized trials (CRTs) rely on the number of clusters and individuals per cluster for statistical power.
  • Variations in cluster sizes can significantly impact study power.
  • Existing methods for addressing variable cluster sizes include design effects or relative efficiency assessments.

Purpose of the Study:

  • Define relative efficiency for unequal versus equal cluster sizes using noncentrality parameters.
  • Investigate the properties of this noncentrality parameter-based measure.
  • Propose a novel approach for adjusting sample size in CRTs with variable cluster sizes.

Main Methods:

  • Focus on two-group comparisons with normally distributed outcomes using a t-test.
  • Utilize the noncentrality parameter to define relative efficiency.
  • Calculate required sample size for unequal cluster size trials to match the power of equal cluster size trials.

Main Results:

  • The noncentrality parameter provides a straightforward and interpretable measure of relative efficiency.
  • The approach directly links required mean cluster size to sample size requirements for equal cluster sizes.
  • Sample size adjustments can be made to mean cluster size alone or simultaneously with the number of clusters.

Conclusions:

  • The proposed noncentrality parameter-based relative efficiency offers a flexible sample size adjustment method for CRTs.
  • This approach complements existing methods and provides a useful alternative for researchers.
  • The defined relative efficiency may differ from existing literature measures under certain conditions.