The human microbial communities are associated with many human diseases such as obesity, diabetes and inflammatory bowel disease. High-throughput sequencing technology has been widely used to quantify the microbial composition in order to understand its impacts on human health. Longitudinal measurements of microbial communities are commonly obtained in many microbiome studies. A key question in such microbiome studies is to identify the microbes that are associated with clinical outcomes or environmental factors. However, microbiome compositional data are highly skewed, bounded in [0,1), and often sparse with many zeros. In addition, the observations from repeated measures in longitudinal studies are correlated. A method that takes into account these features is needed for association analysis in longitudinal microbiome data.