title: Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data.
abstract: Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard parametric and non-parametric models, hurdle models, and zero inflated models. We examine varying degrees of zero inflation, with or without dispersion in the count component, as well as different magnitude and direction of the covariate effect on structural zeros and the count components. We focus on the assessment of type I error, power to detect the overall covariate effect, measures of model fit, and bias and effectiveness of parameter estimations. We also evaluate the abilities of model selection strategies using Akaike information criterion (AIC) or Vuong test to identify the correct model. The simulation studies show that hurdle and zero inflated models have well controlled type I errors, higher power, better goodness of fit measures, and are more accurate and efficient in the parameter estimation. Besides that, the hurdle models have similar goodness of fit and parameter estimation for the count component as their corresponding zero inflated models. However, the estimation and interpretation of the parameters for the zero components differs, and hurdle models are more stable when structural zeros are absent. We then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects.
authors.affiliation: Dalla Lana School of Public Health, University of Toronto, ON, M5T 3M7, Canada.
authors.forename: Lizhen
authors.initials: L
authors.lastname: Xu
authors.affiliation: Dalla Lana School of Public Health, University of Toronto, ON, M5T 3M7, Canada; Program in Genetics and Genome Biology, the Hospital for Sick Children Toronto, ON, M5G 0A4, Canada.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Paterson
authors.affiliation: Division of Gastroenterology, Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, ON, M5T 3L9, Canada; Department of Medicine, University of Toronto, ON, M5S 1A8, Canada.
authors.forename: Williams
authors.initials: W
authors.lastname: Turpin
authors.affiliation: Dalla Lana School of Public Health, University of Toronto, ON, M5T 3M7, Canada; Department of Biostatistics, Princess Margaret Hospital, 610 University Avenue, Toronto, ON, M5G 2M9, Canada.
authors.forename: Wei
authors.initials: W
authors.lastname: Xu
mesh_terms: D015982:Bias; D000069196:Gastrointestinal Microbiome / Q000502:physiology*; D041981:Gastrointestinal Tract / Q000382:microbiology*; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis