Factorial designs: a graphical aid for choosing study designs accounting for interaction

Area of Science:

• Biostatistics
• Clinical Trial Design
• Epidemiology

Background:

• Treatment interactions, whether synergistic or antagonistic, significantly impact factorial study design.
• Existing methods may lack sufficient statistical power to detect interaction effects, especially in prespecified subgroup analyses.

Purpose of the Study:

• To develop a graphical aid for evaluating statistical power loss due to treatment interactions in factorial studies.
• To provide a method for determining optimal sample sizes in subgroups to ensure adequate power for detecting interaction effects.

Main Methods:

• Development of a graphical technique to visualize power loss associated with interaction effects.
• Application of the technique to a published 2x2 factorial study to illustrate its utility.
• Methodology adaptable for designing studies with prespecified subgroup analyses.

Main Results:

• The presence of interaction effects can substantially reduce the power to detect significant main effects in factorial studies.
• The developed graphical aid effectively illustrates this power reduction.
• The technique aids in sample size calculations for subgroups to enhance power for interaction detection.

Conclusions:

• Careful consideration of potential treatment interactions is essential during the design phase of factorial studies.
• Alternative study designs, such as three-arm studies, may be more powerful than 2x2 factorials when interactions are anticipated.
• The proposed graphical method and sample size guidance can improve the statistical rigor of clinical trial design and subgroup analysis.