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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...
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:
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...
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: 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...

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Sample size calculations for 3-level cluster randomized trials.

Steven Teerenstra1, Mirjam Moerbeek, Theo van Achterberg

  • 1Department of Epidemiology, Biostatistics and Health Technology Assessment, Radboud University Nijmegen Medical Centre, Nigmegen, The Netherlands. s.teerenstra@ebh.umcn.nl

Clinical Trials (London, England)
|October 2, 2008
PubMed
Summary
This summary is machine-generated.

This study provides a sample size formula for three-level cluster randomized trials, guiding the number of clusters, subjects, and evaluations for optimal power and resource allocation.

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Area of Science:

  • Health Research Methodology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Three-level cluster randomized trials (CRTs) are emerging in health research, involving strategies, professionals, and patient outcomes.
  • These trials compare implementation strategies across healthcare units, affecting professionals' behavior and patient outcomes.

Purpose of the Study:

  • To provide guidance on determining the number of clusters, subjects per cluster, and evaluations per subject in three-level CRTs.
  • To develop a sample size formula and analyze sample allocation's impact on statistical power and required cluster numbers.

Main Methods:

  • Derived a sample size formula incorporating variance inflation factors (VIFs) for nested data structures.
  • Investigated the influence of sample allocation on statistical power and the number of clusters needed.
  • Related VIFs to interpretable Pearson correlations, allowing incorporation of subject matter knowledge.

Main Results:

  • Required sample size is a product of a baseline size and two VIFs, accounting for clustering at subject and cluster levels.
  • Pearson correlations within VIFs can be informed by subject matter expertise or linked to intraclass correlations (ICCs) from two-level CRTs.
  • Developed formulas for sample allocation to minimize total sample size, minimize cluster count, or maximize power under budget constraints.

Conclusions:

  • Lack of empirical variance component or ICC estimates from three-level CRTs hinders reliable power calculations.
  • Parameterized VIFs offer quantitative insights into how sample size components affect power.
  • Subject matter knowledge and ICCs from two-level CRTs can inform sample size calculations when three-level data is scarce.