Testing latent class of subjects with structural zeros in negative binomial models with applications to gut microbiome data

负二项分布 计数数据 过度分散 准似然 二项检验 统计 瓦尔德试验 似然比检验 数学 计量经济学 计算机科学 统计假设检验 泊松分布
作者
Peng Ye,Xinhui Qiao,Wan Tang,Chunyi Wang,Hua He
出处
期刊:Statistical Methods in Medical Research [SAGE Publishing]
卷期号:31 (11): 2237-2254 被引量:1
标识
DOI:10.1177/09622802221115881
摘要

Human microbiome research has become a hot-spot in health and medical research in the past decade due to the rapid development of modern high-throughput. Typical data in a microbiome study consisting of the operational taxonomic unit counts may have over-dispersion and/or structural zero issues. In such cases, negative binomial models can be applied to address the over-dispersion issue, while zero-inflated negative binomial models can be applied to address both issues. In practice, it is essential to know if there is zero-inflation in the data before applying negative binomial or zero-inflated negative binomial models because zero-inflated negative binomial models may be unnecessarily complex and difficult to interpret, or may even suffer from convergence issues if there is no zero-inflation in the data. On the other hand, negative binomial models may yield invalid inferences if the data does exhibit excessive zeros. In this paper, we develop a new test for detecting zero-inflation resulting from a latent class of subjects with structural zeros in a negative binomial regression model by directly comparing the amount of observed zeros with what would be expected under the negative binomial regression model. A closed form of the test statistic as well as its asymptotic properties are derived based on estimating equations. Intensive simulation studies are conducted to investigate the performance of the new test and compare it with the classical Wald, likelihood ratio, and score tests. The tests are also applied to human gut microbiome data to test latent class in microbial genera.
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