Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data.
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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 then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects.
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PubMed Central - PubMed
Affiliation: Dalla Lana School of Public Health, University of Toronto, ON, M5T 3M7, Canada.
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. No MeSH data available. |
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Mentions: Fig 7 shows the boxplots of estimations and their SEs for β1 using ZI models and for using hurdle models when ZINB simulated data has 20% zero inflation in the unexposed group and γ1 = 0.4. The true values of are derived from the parameter estimations of the ZI model using Equation 1 and 2. Results for other simulation settings are shown in S4 Fig, S5 Fig, S6 Fig, S7 Fig. Because the logistic regression part is the same, the estimations for are identical across different hurdle models. Similarly to the case of γ1, ZINB has unbiased estimation of β1 for both ZIP and ZINB distributed data, while ZIP is only unbiased for ZIP distributed data. Notice that when the proportion of zero inflation is low (e.g., ϕc = 20%), ZINB may have unstable results with some large SE. The estimations are more stable when the zero inflation proportion increases to 50% or when the sample size is increased (results not given). On the contrary, hurdle models give unbiased and stable estimates for in all simulation scenarios. |
View Article: PubMed Central - PubMed
Affiliation: Dalla Lana School of Public Health, University of Toronto, ON, M5T 3M7, Canada.
No MeSH data available.