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Cluster Sampling Method

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.
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Sampling Plans

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Randomized Experiments

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
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Sample Size Calculation

Sample Size Calculation

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Group Design

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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Related Experiment Video

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Re-estimating sample size in cluster randomised trials with active recruitment within clusters.

S van Schie¹, M Moerbeek

¹Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands.

Statistics in Medicine

|April 11, 2014

Summary

An internal pilot study can help adjust sample size calculations in cluster randomized trials when variances are unknown. This method improves precision for the intracluster correlation coefficient, ensuring adequate statistical power.

Keywords:

a priori power analysis hierarchical data intracluster correlation coefficient type I error

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Area of Science:

Biostatistics
Clinical Trials

Background:

Cluster randomized trials often face challenges with unknown variance components at the cluster and individual levels, impacting sample size efficiency.
Active recruitment within clusters is feasible, but precise sample size determination requires prior knowledge of variance parameters.

Purpose of the Study:

To propose and evaluate the utility of an internal pilot study design for cluster randomized trials.
To address the issue of unknown variances and their impact on sample size and statistical power.

Main Methods:

Utilizing simulated data to assess the performance of an internal pilot study design.
Investigating the re-estimation of variances and the intracluster correlation coefficient (ICC).
Examining the impact of the internal pilot on sample size recalculation and power adjustment.

Main Results:

An internal pilot study allows for the re-estimation of variances and recalculation of sample size during the trial.
Simulations demonstrate that power can be adjusted effectively, maintaining an acceptable Type I error rate.
Re-estimation of the intracluster correlation coefficient (ICC) with greater precision positively influences sample size determination.

Conclusions:

An internal pilot study design is a viable strategy for cluster randomized trials when active recruitment is possible within a limited number of clusters.
This approach enhances the efficiency of sample size determination by addressing unknown variance components.
The internal pilot study facilitates adaptive sample size adjustments, preserving study power and statistical integrity.