Related Experiment Video
Updated: Feb 12, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
Published on: January 28, 2014
Invited Commentary: The Microbiome and Population Health-Considerations for Enhancing Study Design and Data Analysis
1Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota.
Abstract:
Measurement and characterization of the human microbiome in large population-based human studies has recently become a reality secondary to technological advances in high-throughput DNA sequencing. These advances bring new challenges and knowledge gaps for study planning, data analysis, and interpretation that are novel to large-scale epidemiologic studies. In this issue of the Journal, Sinha et al. (Am J Epidemiol. 2018;187(6):1282-1290) have provided data with which to inform statistical power and sample size requirements for microbiome studies in population-based settings. This work serves as a helpful starting point for study planning while also serving as a springboard for discussion regarding additional considerations for improving microbiome research. This commentary emphasizes the importance of selecting microbiome metrics appropriate for the biological hypothesis under investigation, as well as the need for new analytical tools that can better capitalize on the unique yet rich information contained in microbiome data sets.
More Related Videos
Related Concept Videos
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Design Consideration
The factor of safety is another key...
Analysis of Population Pharmacokinetic Data
Transmission Line Design Considerations
Data Collection by Observations
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Statistical Methods for Analyzing Epidemiological Data

