• Merck Research Laboratories, Rahway, New Jersey, United States of America.
• Fusion Data Analytics Lab, School of Industrial Management Engineering, Korea University, Seoul, South Korea.
• Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
• Department of Population Health (Biostatistics), NYU Langone Medical Center, New York, NY, United States of America.

Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study. Here, we propose a multivariate distance-based test to evaluate the association between key phenotypic variables and microbial interdependence utilizing the repeatedly measured microbiome data. Extensive simulations were performed to evaluate the validity and efficiency of the proposed method. We also demonstrate the utility of the proposed test using a well-designed longitudinal murine experiment and a longitudinal human study. The proposed methodology has been implemented in the freely distributed open-source R package and Python code.