The mixed model for the analysis of a repeated‐measurement multivariate count data

Background: Chronic non-communicable diseases:- such as epilepsy, are increasingly recognized as public health problems in developing and African countries. This study aimed at finding determinants of the number of epileptic seizure attacks using different count data modeling techniques.Methods: Four common fixed-effects Poisson family models were reviewed to analyze the count data with a high proportion of zeros in longitudinal outcome, i.e., the number of seizure attacks in epilepsy patients. This is because, in addition to the problem of extra zeros, the correlation between measurements upon the same patient at different occasions needs to be taken into consideration.Results: The investigation remarkably identified some important factors associated with epileptic seizure attacks. As people grow old , the number of seizure attacks increased and male patients had more seizures than their female counterparts. In general, a patient’s age, sex, monthly income, family history of epilepsy andservice satisfaction were some of the significant factors responsible for the frequency of seizure attacks (P value<0.05).Conclusion: This study suggests that zero-inflated negative binomial is the best model for predicting and describing the number of seizure attacks as well as identifying the potential risk factors. Addressing these risk factors will definitely contain the progression of seizure attack.Keywords: linear mixed model, hurdle model, seizure attacks, zero-inflated models.

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A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data

10.1101/019562 ◽

2015 ◽

Author(s):

Amanda J Lea ◽

Jenny Tung ◽

Xiang Zhou

Keyword(s):

Dna Methylation ◽

Count Data ◽

Mixed Model ◽

Bisulfite Sequencing ◽

Structured Populations ◽

P Value ◽

Sequencing Data ◽

Data Set ◽

Age Related ◽

Bisulfite Sequencing Data

Identifying sources of variation in DNA methylation levels is important for understanding gene regulation. Recently, bisulfite sequencing has become a popular tool for investigating DNA methylation levels. However, modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples, and because of the computational challenges of controlling for genetic covariance in count data. To address these challenges, we present a binomial mixed model and an efficient, sampling-based algorithm (MACAU: Mixed model association for count data via data augmentation) for approximate parameter estimation and p-value computation. This framework allows us to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. Using simulations and two real data sets (whole genome bisulfite sequencing (WGBS) data from Arabidopsis thaliana and reduced representation bisulfite sequencing (RRBS) data from baboons), we show that our method provides well-calibrated test statistics in the presence of population structure. Further, it improves power to detect differentially methylated sites: in the RRBS data set, MACAU detected 1.6-fold more age-associated CpG sites than a beta-binomial model (the next best approach). Changes in these sites are consistent with known age-related shifts in DNA methylation levels, and are enriched near genes that are differentially expressed with age in the same population. Taken together, our results indicate that MACAU is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations. MACAU is freely available at www.xzlab.org/software.html.

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Penalized composite link models for aggregated spatial count data: A mixed model approach

Spatial Statistics ◽

10.1016/j.spasta.2016.06.007 ◽

2016 ◽

Vol 17 ◽

pp. 179-198 ◽

Cited By ~ 2

Author(s):

Diego Ayma ◽

María Durbán ◽

Dae-Jin Lee ◽

Paul H.C. Eilers

Keyword(s):

Count Data ◽

Mixed Model ◽

Mixed Model Approach ◽

Spatial Count Data ◽

Model Approach

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Soil Chemical Properties Barely Perturb the Abundance of Entomopathogenic Fusarium oxysporum: A Case Study Using a Generalized Linear Mixed Model for Microbial Pathogen Occurrence Count Data

Pathogens ◽

10.3390/pathogens7040089 ◽

2018 ◽

Vol 7 (4) ◽

pp. 89 ◽

Cited By ~ 5

Author(s):

Lav Sharma ◽

Irene Oliveira ◽

Fernando Raimundo ◽

Laura Torres ◽

Guilhermina Marques

Keyword(s):

Fusarium Oxysporum ◽

Count Data ◽

Mixed Model ◽

Linear Mixed Model ◽

Organic Matter Content ◽

Chemical Properties ◽

Generalized Linear Mixed Model ◽

Soil Chemical Properties ◽

Available Phosphorus ◽

Fungal Abundance

Fusarium oxysporum exhibits insect pathogenicity—however, generalized concerns of releasing phytopathogens within agroecosystems marred its entomopathogenicity-related investigations. In a previous study, soils were sampled from Douro vineyards and adjacent hedgerows. In this study, 80 of those soils were analyzed for their chemical properties and were subsequently co-related with the abundance of entomopathogenic F. oxysporum, after insect baiting of soils with Galleria mellonella and Tenebrio molitor larvae. The soil chemical properties studied were organic matter content; total organic carbon; total nitrogen; available potassium; available phosphorus; exchangeable cations, such as K+, Na+, Ca2+, and Mg2+; pH; total acidity; degree of base saturation; and effective cation exchange capacity. Entomopathogenic F. oxysporum was found in 48 soils, i.e., 60% ± 5.47%, of the total soil samples. Out of the 1280 insect larvae used, 93, i.e., 7.26% ± 0.72%, were found dead by entomopathogenic F. oxysporum. Stepwise deletion of non-significant variables using a generalized linear model was followed by a generalized linear mixed model (GLMM). A higher C:N (logarithmized) (p < 0.001) and lower exchangeable K+ (logarithmized) (p = 0.008) were found significant for higher fungal abundance. Overall, this study suggests that entomopathogenic F. oxysporum is robust with regard to agricultural changes, and GLMM is a useful statistical tool for count data in ecology.

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The Analysis of Variance Mixed Model with Allocated Observations: Application to Repeated Measurement Designs

Journal of the American Statistical Association ◽

10.1080/01621459.1981.10477692 ◽

1981 ◽

Vol 76 (375) ◽

pp. 607-611 ◽

Cited By ~ 6

Author(s):

Juliet Popper Shaffer

Keyword(s):

Analysis Of Variance ◽

Mixed Model ◽

Repeated Measurement

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Multiresolution spatial generalized linear mixed model for integrating multi-fidelity spatial count data without common identifiers between data sources

Spatial Statistics ◽

10.1016/j.spasta.2020.100467 ◽

2020 ◽

Vol 39 ◽

pp. 100467

Author(s):

Sungil Kim ◽

Rong Duan ◽

Guang-Qin Ma ◽

Heeyoung Kim

Keyword(s):

Count Data ◽

Mixed Model ◽

Linear Mixed Model ◽

Generalized Linear Mixed Model ◽

Data Sources ◽

Spatial Count Data

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A comparative study of different covariance structure models for the analysis of repeated measurement data

International Journal of Biomathematics ◽

10.1142/s1793524517500073 ◽

2016 ◽

Vol 10 (01) ◽

pp. 1750007

Author(s):

Yazhou Wu ◽

Ling Zhang ◽

Liang Zhou ◽

Xiaoyu Liu ◽

Ling Liu ◽

...

Keyword(s):

Mixed Model ◽

Linear Mixed Model ◽

Random Effect ◽

Measurement Data ◽

Covariance Structure ◽

Repeated Measurement ◽

Covariance Structures ◽

Analysis And Design ◽

The One ◽

The Individual

In repeated measurement data, the variables are not independent, and a certain auto-correlation typically exists between different levels of repeated measurement factors. The random error is composed of at least two parts, i.e. the individual random effect and the intra-individual multi-repeated measurement effect. Traditional statistical analysis methods (such as the [Formula: see text]-test and the one-way analysis of variance) are not applicable. The linear mixed model has been widely applied for the analysis and design of repeated measurement data. This paper focuses on medical examples and describes the selection of a covariance structure for the linear mixed model of repeated measurement in the modeling of different variance–covariance structures. By selecting different covariance structures, we can perform the parameter estimation and statistical test for the fixed effect of repeated measurement data, the parameters of random effects, and the covariance matrix. The results are analyzed and compared to provide a reference for applying the linear mixed model of repeated measurement to medical research.

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Supplemental Material for Analyzing Discontinuities in Longitudinal Count Data: A Multilevel Generalized Linear Mixed Model

Psychological Methods ◽

10.1037/met0000347.supp ◽

2020 ◽

Keyword(s):

Count Data ◽

Mixed Model ◽

Linear Mixed Model ◽

Generalized Linear Mixed Model ◽

Longitudinal Count Data

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A Flexible Mixed Model for Clustered Count Data

Stats ◽

10.3390/stats5010004 ◽

2022 ◽

Vol 5 (1) ◽

pp. 52-69

Author(s):

Darcy Steeg Morris ◽

Kimberly F. Sellers

Keyword(s):

Regression Model ◽

Random Effects ◽

Count Data ◽

Mixed Models ◽

Poisson Regression ◽

Mixed Model ◽

Random Effect ◽

Model Fit ◽

Cross Sectional ◽

Special Cases

Clustered count data are commonly modeled using Poisson regression with random effects to account for the correlation induced by clustering. The Poisson mixed model allows for overdispersion via the nature of the within-cluster correlation, however, departures from equi-dispersion may also exist due to the underlying count process mechanism. We study the cross-sectional COM-Poisson regression model—a generalized regression model for count data in light of data dispersion—together with random effects for analysis of clustered count data. We demonstrate model flexibility of the COM-Poisson random intercept model, including choice of the random effect distribution, via simulated and real data examples. We find that COM-Poisson mixed models provide comparable model fit to well-known mixed models for associated special cases of clustered discrete data, and result in improved model fit for data with intermediate levels of over- or underdispersion in the count mechanism. Accordingly, the proposed models are useful for capturing dispersion not consistent with commonly used statistical models, and also serve as a practical diagnostic tool.

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