Best (but oft forgotten) practices: Efficient sample sizes for commonly used trial designs.

Area of Science:

• Biostatistics
• Clinical Trial Design
• Research Methodology

Background:

• High statistical power is crucial for study quality and reproducibility.
• Resource constraints necessitate efficient study designs.
• Optimal allocation of subjects and resources is key in randomized trials.

Purpose of the Study:

• To present study designs that minimize subjects or budget for a desired power level in randomized trials.
• To introduce maximin designs that guarantee power across parameter ranges and minimize costs.
• To focus on continuous outcomes in parallel, crossover, and nested trial designs.

Main Methods:

• Developing optimal designs for subject allocation and resource use.
• Implementing maximin designs to account for unknown parameters like outcome variances.
• Applying these designs to 2-group parallel, AB/BA crossover, cluster-randomized, and multicenter trials.

Main Results:

• Demonstrated methods for calculating sample sizes for optimal and maximin designs.
• Illustrated calculations with nutrition study examples.
• Discussed computer programs for sample size calculations and optimal designs for various outcomes.

Conclusions:

• Optimal and maximin designs enhance the efficiency and reliability of randomized clinical trials.
• These approaches are vital for resource-conscious research, particularly with continuous outcomes.
• The presented methods and tools aid researchers in achieving desired statistical power effectively.