Key Considerations for Cluster Randomized Designs

Free Webinar

Power and sample size for two-level, three-level and stepped-wedge designs

Individual randomization is the standard approach to randomization in clinical trials. However, logistical and practical challenges mean that other randomization schemes may be preferred. Cluster randomization, where all subjects in a cluster, such as a hospital or school, are randomly assigned to one group, is a common method which can help reduce costs and prevent bias but adds analysis complexity and reduces power vs individual randomization. 

In this webinar, we will cover the differences between individual and cluster randomization, the advantages and disadvantages of cluster randomization and how to find the appropriate sample size for various types of cluster randomization schemes.

Learning objectives:

In this free webinar,  you will learn how to navigate the complexities of cluster randomized designs, empowering you to implement methodologically sound and efficient studies.

Key Areas Covered:

1. Understanding Cluster Randomized Designs

• Recognize the practical advantages of cluster randomization (e.g., reduced contamination, logistical feasibility, administrative convenience).

• Identify the key statistical challenges, including reduced power and the need to account for within-cluster correlation (Intracluster Correlation Coefficient - ICC).

2. The Impact on Power and Sample Size

• Learn how the ICC and cluster size influence the design effect and drastically impact the required sample size.

• Understand the difference between powering a trial based on the number of clusters versus the number of subjects.

3. Designing Different Cluster Randomization Schemes

• Apply principles to design:
  • Two-level designs (subjects within clusters).
  • Three-level designs (e.g., subjects within classrooms within schools).
  • Stepped-wedge designs where clusters cross over from control to intervention over time.

• Interpret how the choice of design affects the trial's timeline, power, and analytical model.

4. Practical and Analytical Considerations

• Address the logistical challenges of implementing cluster randomization (e.g., recruitment variation across clusters, ethical considerations).
• Understand the basic analytical approaches required for correlated data from cluster designs.

5. Tools and Best Practices

• Discover how nQuery streamlines sample size calculation for various cluster randomized trials.

• Compare the implications of different design choices to optimize feasibility and robustness.

nQuery helps make your clinical trials faster, less costly and more successful. So if you need something more than just a sample size calculator, nQuery is an end-to-end platform covering Frequentist, Bayesian, and Adaptive designs with 1000+ sample size procedures. 

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