Bayesian Hierarchical Random Intercept Model Based on Three Parameter Gamma Distribution

Abstract Motivation Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. Results We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. Availability and implementation https://github.com/takumorizo/OHVarfinDer. Supplementary information Supplementary data are available at Bioinformatics online.

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Environmental policy performance and its determinants: Application of a three-level random intercept model

Energy Policy ◽

10.1016/j.enpol.2017.11.053 ◽

2018 ◽

Vol 114 ◽

pp. 134-144 ◽

Cited By ~ 6

Author(s):

Marzio Galeotti ◽

Yana Rubashkina ◽

Silvia Salini ◽

Elena Verdolini

Keyword(s):

Environmental Policy ◽

Policy Performance ◽

Random Intercept ◽

Random Intercept Model

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The change of Barthel Index scores from the time of discharge until 3-month post-discharge among acute stroke patients in Malaysia: A random intercept model

PLoS ONE ◽

10.1371/journal.pone.0208594 ◽

2018 ◽

Vol 13 (12) ◽

pp. e0208594 ◽

Cited By ~ 6

Author(s):

Kamarul Imran Musa ◽

Thomas J. Keegan

Keyword(s):

Acute Stroke ◽

Barthel Index ◽

Stroke Patients ◽

Post Discharge ◽

Random Intercept ◽

Random Intercept Model

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Multilevel Analysis of Determinants of Anemia Prevalence among Children Aged 6–59 Months in Ethiopia: Classical and Bayesian Approaches

Anemia ◽

10.1155/2018/3087354 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 10

Author(s):

Kemal N. Kawo ◽

Zeytu G. Asfaw ◽

Negusse Yohannes

Keyword(s):

Public Health ◽

Drinking Water ◽

Multilevel Analysis ◽

Health Problem ◽

Public Health Problem ◽

Wealth Index ◽

Bayesian Approaches ◽

Random Intercept ◽

Random Intercept Model ◽

Prevalence Of Anemia

Background. Anemia is a widely spread public health problem and affects individuals at all levels. However, there is a considerable regional variation in its distribution. Objective. Thus, this study aimed to assess and model the determinants of prevalence of anemia among children aged 6–59 months in Ethiopia. Data. Cross-sectional data from Ethiopian Demographic and Health Survey was used for the analysis. It was implemented by the Central Statistical Agency from 27 December 2010 through June 2011 and the sampling technique employed was multistage. Method. The statistical models that suit the hierarchical data such as variance components model, random intercept model, and random coefficients model were used to analyze the data. Likelihood and Bayesian approaches were used to estimate both fixed effects and random effects in multilevel analysis. Result. This study revealed that the prevalence of anemia among children aged between 6 and 59 months in the country was around 42.8%. The multilevel binary logistic regression analysis was performed to investigate the variation of predictor variables of the prevalence of anemia among children aged between 6 and 59 months. Accordingly, it has been identified that the number of children under five in the household, wealth index, age of children, mothers’ current working status, education level, given iron pills, size of child at birth, and source of drinking water have a significant effect on prevalence of anemia. It is found that variances related to the random term were statistically significant implying that there is variation in prevalence of anemia across regions. From the methodological aspect, it was found that random intercept model is better compared to the other two models in fitting the data well. Bayesian analysis gave consistent estimates with the respective multilevel models and additional solutions as posterior distribution of the parameters. Conclusion. The current study confirmed that prevalence of anemia among children aged 6–59 months in Ethiopia was severe public health problem, where 42.8% of them are anemic. Thus, stakeholders should pay attention to all significant factors mentioned in the analysis of this study but wealth index/improving household income and availability of pure drinking water are the most influential factors that should be improved anyway.

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