Power and sample-size estimation in human microbiome research

Area of Science:

• Microbiology
• Bioinformatics
• Statistical Genetics

Background:

• Human microbiome research is crucial for understanding complex diseases like diabetes, IBD, and cancer.
• High-throughput metagenomic sequencing is commonly used for comparing microbial communities in health and disease.
• Microbiome data presents unique challenges due to its compositional, sparse, and zero-inflated nature.

Purpose of the Study:

• To synthesize current approaches for study design and sample-size estimation in microbiome research.
• To provide practical guidance for clinicians and researchers navigating statistical complexities.
• To address the difficulties in robust cohort design and power-based sample-size estimation for microbiome data.

Main Methods:

• Review of existing literature on microbiome study design.
• Synthesis of statistical methodologies for microbiome data analysis.
• Exploration of analytical frameworks from diversity indices to causal inference.

Main Results:

• Microbiome data's properties (compositional, sparse, zero-inflated) complicate statistical modeling.
• These complexities often lead to inflated sample-size requirements.
• Diverse analytical frameworks have different statistical assumptions and are optimized for specific hypotheses.

Conclusions:

• Effective study design and sample-size estimation are critical for valid microbiome research.
• Understanding the statistical nuances of microbiome data is essential for accurate interpretation.
• This review aims to equip researchers with the knowledge to overcome common statistical hurdles in microbiome studies.