PDF(Pubmed)
Abstract：
BACKGROUND: Schistosomiasis is a neglected disease prevalent in tropical and sub-tropical areas of the world, especially in Africa. Detecting the presence of the disease is based on the detection of the parasites in the stool or urine of children and adults. In such studies, typically, data collected on schistosomiasis infection includes information on many negative individuals leading to a high zero inflation. Thus, in practice, counts data with excessive zeros are common. However, the purpose of this analysis is to apply statistical models to the count data and evaluate their performance and results.
METHODS: This is a secondary analysis of previously collected data. As part of a modelling process, a comparison of the Poisson regression, negative binomial regression and their associated zero inflated and hurdle models were used to determine which offered the best fit to the count data.
RESULTS: Overall, 94.1% of the study participants did not have any schistosomiasis eggs out of 1345 people tested, resulting in a high zero inflation. The performance of the negative binomial regression models (hurdle negative binomial (HNB), zero inflated negative binomial (ZINB) and the standard negative binomial) were better than the Poisson-based regression models (Poisson, zero inflated Poisson, hurdle Poisson). The best models were the ZINB and HNB and their performances were indistinguishable according to information-based criteria test values.
CONCLUSIONS: The zero-inflated negative binomial and hurdle negative binomial models were found to be the most satisfactory fit for modelling the over-dispersed zero inflated count data and are recommended for use in future statistical modelling analyses.
METHODS: This is a secondary analysis of previously collected data. As part of a modelling process, a comparison of the Poisson regression, negative binomial regression and their associated zero inflated and hurdle models were used to determine which offered the best fit to the count data.
RESULTS: Overall, 94.1% of the study participants did not have any schistosomiasis eggs out of 1345 people tested, resulting in a high zero inflation. The performance of the negative binomial regression models (hurdle negative binomial (HNB), zero inflated negative binomial (ZINB) and the standard negative binomial) were better than the Poisson-based regression models (Poisson, zero inflated Poisson, hurdle Poisson). The best models were the ZINB and HNB and their performances were indistinguishable according to information-based criteria test values.
CONCLUSIONS: The zero-inflated negative binomial and hurdle negative binomial models were found to be the most satisfactory fit for modelling the over-dispersed zero inflated count data and are recommended for use in future statistical modelling analyses.
摘要：
背景：血吸虫病是一种被忽视的疾病，在世界热带和亚热带地区流行，尤其是在非洲。检测疾病的存在是基于对儿童和成人的粪便或尿液中的寄生虫的检测。在这样的研究中,通常，收集的血吸虫病感染数据包括许多负面个体的信息，导致高零通货膨胀。因此，在实践中,计数数据中过多的零是常见的。然而,此分析的目的是将统计模型应用于计数数据并评估其性能和结果。
方法：这是对先前收集的数据的二次分析。作为建模过程的一部分，比较泊松回归，使用负二项回归及其相关的零膨胀和障碍模型来确定哪种模型最适合计数数据。
结果：总体而言，在接受测试的1345人中，94.1%的研究参与者没有任何血吸虫病卵。导致零通胀。负二项回归模型的性能(跨栏负二项(HNB)，零膨胀负二项(ZINB)和标准负二项)优于基于泊松的回归模型(泊松，零膨胀泊松，跨栏泊松）。最佳模型是ZINB和HNB，根据基于信息的标准测试值，它们的性能无法区分。
结论：发现零膨胀负二项和障碍负二项模型是对过度分散的零膨胀计数数据进行建模的最令人满意的拟合，建议在未来的统计建模分析中使用。
方法：这是对先前收集的数据的二次分析。作为建模过程的一部分，比较泊松回归，使用负二项回归及其相关的零膨胀和障碍模型来确定哪种模型最适合计数数据。
结果：总体而言，在接受测试的1345人中，94.1%的研究参与者没有任何血吸虫病卵。导致零通胀。负二项回归模型的性能(跨栏负二项(HNB)，零膨胀负二项(ZINB)和标准负二项)优于基于泊松的回归模型(泊松，零膨胀泊松，跨栏泊松）。最佳模型是ZINB和HNB，根据基于信息的标准测试值，它们的性能无法区分。
结论：发现零膨胀负二项和障碍负二项模型是对过度分散的零膨胀计数数据进行建模的最令人满意的拟合，建议在未来的统计建模分析中使用。
