Human microbiome studies based on genetic sequencing techniques produce
compositional longitudinal data of the relative abundances of microbial taxa
over time, allowing to understand, through mixed-effects modeling, how
microbial communities evolve in response to clinical interventions,
environmental changes, or disease progression. In particular, the Zero-Inflated
Beta Regression (ZIBR) models jointly and over time the presence and abundance
of each microbe taxon, considering the compositional nature of the data, its
skewness, and the over-abundance of zeros. However, as for other complex random
effects models, maximum likelihood estimation suffers from the intractability
of likelihood integrals. Available estimation methods rely on log-likelihood
approximation, which is prone to potential limitations such as biased estimates
or unstable convergence. In this work we develop an alternative maximum
likelihood estimation approach for the ZIBR model, based on the Stochastic
Approximation Expectation Maximization (SAEM) algorithm. The proposed
methodology allows to model unbalanced data, which is not always possible in
existing approaches. We also provide estimations of the standard errors and the
log-likelihood of the fitted model. The performance of the algorithm is
established through simulation, and its use is demonstrated on two microbiome
studies, showing its ability to detect changes in both presence and abundance
of bacterial taxa over time and in response to treatment.

Este artículo explora los viajes en el tiempo y sus implicaciones.

Descargar PDF: