scholarly journals Application of zero-inflated negative binomial mixed model to human microbiota sequence data

2014 ◽  
Author(s):  
Rui Fang ◽  
Brandie Wagner ◽  
J. Kirk Harris ◽  
Sophie A Fillon
Keyword(s):  
Random Effects ◽  
Repeated Measures ◽  
High Throughput Sequencing ◽  
Mixed Model ◽  
Negative Binomial ◽  
Sequence Data ◽  
Repeated Measurements ◽  
Parameter Estimates ◽  
Human Microbiota ◽  
Negative Binomial Mixed Model

Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. However, these data consist of non-negative, highly skewed sequence counts with a large proportion of zeros. Zero-inflated models are useful for analyzing such data. Moreover, the non-zero observations may be over-dispersed in relation to the Poisson distribution, biasing parameter estimates and underestimating standard errors. In such a circumstance, a zero-inflated negative binomial (ZINB) model better accounts for these characteristics compared to a zero-inflated Poisson (ZIP). In addition, complex study designs are possible with repeated measurements or multiple samples collected from the same subject, thus random effects are introduced to account for the within subject variation. A zero-inflated negative binomial mixed model contains components to model the probability of excess zero values and the negative binomial parameters, allowing for repeated measures using independent random effects between these two components. The objective of this study is to examine the application of a zero-inflated negative binomial mixed model to human microbiota sequence data.

scholarly journals Application of zero-inflated negative binomial mixed model to human microbiota sequence data

2014 ◽  
Author(s):  
Rui Fang ◽  
Brandie Wagner ◽  
J. Kirk Harris ◽  
Sophie A Fillon
Keyword(s):  
Random Effects ◽  
Repeated Measures ◽  
High Throughput Sequencing ◽  
Mixed Model ◽  
Negative Binomial ◽  
Sequence Data ◽  
Repeated Measurements ◽  
Parameter Estimates ◽  
Human Microbiota ◽  
Negative Binomial Mixed Model

Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. However, these data consist of non-negative, highly skewed sequence counts with a large proportion of zeros. Zero-inflated models are useful for analyzing such data. Moreover, the non-zero observations may be over-dispersed in relation to the Poisson distribution, biasing parameter estimates and underestimating standard errors. In such a circumstance, a zero-inflated negative binomial (ZINB) model better accounts for these characteristics compared to a zero-inflated Poisson (ZIP). In addition, complex study designs are possible with repeated measurements or multiple samples collected from the same subject, thus random effects are introduced to account for the within subject variation. A zero-inflated negative binomial mixed model contains components to model the probability of excess zero values and the negative binomial parameters, allowing for repeated measures using independent random effects between these two components. The objective of this study is to examine the application of a zero-inflated negative binomial mixed model to human microbiota sequence data.

scholarly journals Zero-inflated negative binomial mixed model: an application to two microbial organisms important in oesophagitis

2016 ◽  
Vol 144 (11) ◽  
pp. 2447-2455 ◽  
Author(s):  
R. FANG ◽  
B. D. WAGNER ◽  
J. K. HARRIS ◽  
S. A. FILLON
Keyword(s):  
Microbial Communities ◽  
High Throughput Sequencing ◽  
Mixed Model ◽  
Negative Binomial ◽  
Sequence Data ◽  
Repeated Measurements ◽  
Hierarchical Regression ◽  
Study Designs ◽  
Multiple Samples ◽  
Negative Binomial Mixed Model

SUMMARYAltered microbial communities are thought to play an important role in eosinophilic oesophagitis, an allergic inflammatory condition of the oesophagus. Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. However, these data consist of non-negative, highly skewed sequence counts with a large proportion of zeros. In addition, hierarchical study designs are often performed with repeated measurements or multiple samples collected from the same subject, thus requiring approaches to account for within-subject variation, yet only a small number of microbiota studies have applied hierarchical regression models. In this paper, we describe and illustrate the use of a hierarchical regression-based approach to evaluate multiple factors for a small number of organisms individually. More specifically, the zero-inflated negative binomial mixed model with random effects in both the count and zero-inflated parts is applied to evaluate associations with disease state while adjusting for potential confounders for two organisms of interest from a study of human microbiota sequence data in oesophagitis.

scholarly journals Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 362
Author(s):  
Ioannis Spyroglou ◽  
Jan Skalák ◽  
Veronika Balakhonova ◽  
Zuzana Benedikty ◽  
Alexandros G. Rigas ◽  
...  
Keyword(s):  
Time Series ◽  
Random Effects ◽  
Mixed Models ◽  
Mixed Model ◽  
Repeated Measurements ◽  
Time Series Models ◽  
Additional Time ◽  
Biotic Stresses ◽  
Viable Solution ◽  
Long Time

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.

Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks

Parasitology ◽  
2001 ◽  
Vol 122 (5) ◽  
pp. 563-569 ◽  
Author(s):  
D. A. ELSTON ◽  
R. MOSS ◽  
T. BOULINIER ◽  
C. ARROWSMITH ◽  
X. LAMBIN
Keyword(s):  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Negative Binomial ◽  
Generalized Linear Mixed Model ◽  
Red Grouse ◽  
Worked Example ◽  
Fixed And Random Effects ◽  
Individual Effects ◽  
Lagopus Lagopus Scoticus

The statistical aggregation of parasites among hosts is often described empirically by the negative binomial (Poisson-gamma) distribution. Alternatively, the Poisson-lognormal model can be used. This has the advantage that it can be fitted as a generalized linear mixed model, thereby quantifying the sources of aggregation in terms of both fixed and random effects. We give a worked example, assigning aggregation in the distribution of sheep ticksIxodes ricinuson red grouseLagopus lagopus scoticuschicks to temporal (year), spatial (altitude and location), brood and individual effects. Apparent aggregation among random individuals in random broods fell 8-fold when spatial and temporal effects had been accounted for.

scholarly journals Modeling Relations between Compacted and Uncompacted Crown Ratio for the Northern United States

2019 ◽  
Vol 65 (5) ◽  
pp. 593-601
Author(s):  
James A Westfall ◽  
Megan B E Westfall ◽  
KaDonna C Randolph
Keyword(s):  
United States ◽  
Random Effects ◽  
Goodness Of Fit ◽  
Tree Mortality ◽  
Mixed Model ◽  
The United States ◽  
Repeated Measurements ◽  
Growth Potential ◽  
Model Framework ◽  
Northern United States

Abstract Tree crown ratio is useful in various applications such as prediction of tree mortality probabilities, growth potential, and fire behavior. Crown ratio is commonly assessed in two ways: (1) compacted crown ratio (CCR—lower branches visually moved upwards to fill missing foliage gaps) and (2) uncompacted crown ratio (UNCR—no missing foliage adjustment). The national forest inventory of the United States measures CCR on all trees, whereas only a subset of trees also are assessed for UNCR. Models for 27 species groups are presented to predict UNCR for the northern United States. The model formulation is consistent with those developed for other US regions while also accounting for the presence of repeated measurements and heterogeneous variance in a mixed-model framework. Ignoring random-effects parameters, the fit index values ranged from 0.43 to 0.78, and root mean squared error spanned 0.08–0.15; considerable improvements in both goodness-of-fit statistics were realized via inclusion of the random effects. Comparison of UNCR predictions with models developed for the southern United States exhibited close agreement, whereas comparisons with models used in Forest Vegetation Simulator variants indicated poor association. The models provide additional analytical flexibility for using the breadth of northern region data in applications where UNCR is the appropriate crown characteristic.

scholarly journals Changes in fruit, vegetable and fish consumption after statutory retirement: a prospective cohort study

2020 ◽  
Vol 123 (12) ◽  
pp. 1390-1395
Author(s):  
Kirsi Ali-Kovero ◽  
Olli Pietiläinen ◽  
Elina Mauramo ◽  
Sauli Jäppinen ◽  
Ossi Rahkonen ◽  
...  
Keyword(s):  
Fish Consumption ◽  
Repeated Measures ◽  
Food Habits ◽  
Employment Status ◽  
Mixed Model ◽  
Negative Binomial ◽  
Vegetable Consumption ◽  
Healthy Food ◽  
Health Study

AbstractRetirement is a major life transition affecting health and health behaviour, but evidence on how this transition contributes to changes in healthy food habits is scarce. We examined whether the consumption of fruit and vegetables as well as fish changes after transition into statutory retirement. The data were derived from the prospective Helsinki Health Study. At phase 1 in 2000–2002, all participants were 40- to 60-year-old employees of the City of Helsinki, Finland (n 8960, response rate 67 %). Follow-up surveys were conducted in 2007, 2012 and 2017 (response rates 79–83 %). Using the four phases, we formed three nested cohorts in which the participants either continued working or moved to statutory retirement. The final analytical sample consisted of 6887 participants (14 357 observations). Frequency of fruit, vegetable and fish consumption was calculated from a twenty-two-item FFQ. Analyses of repeated measures of food consumption before and after retirement transition were conducted with a negative binomial mixed model, adjusting for age, marital status, limiting long-standing illness and household income. During the follow-up, altogether 3526 participants retired. Transition to retirement was associated with a decrease in vegetable consumption among women and, contrarily, with an increase in fruit consumption among men (P < 0·05 for interaction between time and employment status). Fish consumption did not differ by the change in employment status. Statutory retirement can have mixed effects on healthy food habits, and these can differ between food groups and sex. Healthy food habits should be promoted among employees transitioning to retirement.

Measuring growth patterns in the field: effects of sampling regime and methods on standardized estimates

2011 ◽  
Vol 89 (6) ◽  
pp. 529-537 ◽  
Author(s):  
J.G.A. Martin ◽  
F. Pelletier
Keyword(s):  
Mixed Models ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Models ◽  
Simulated Data ◽  
Growth Patterns ◽  
Repeated Measurements ◽  
Least Squares Regression ◽  
Sampling Regime ◽  
Parturition Date

Although mixed effects models are widely used in ecology and evolution, their application to standardized traits that change within season or across ontogeny remains limited. Mixed models offer a robust way to standardize individual quantitative traits to a common condition such as body mass at a certain point in time (within a year or across ontogeny), or parturition date for a given climatic condition. Currently, however, most researchers use simple linear models to accomplish this task. We use both empirical and simulated data to underline the application of mixed models for standardizing trait values to a common environment for each individual. We show that mixed model standardizations provide more accurate estimates of mass parameters than linear models for all sampling regimes and especially for individuals with few repeated measures. Our simulations and analyses on empirical data both confirm that mixed models provide a better way to standardize trait values for individuals with repeated measurements compared with classical least squares regression. Linear regression should therefore be avoided to adjust or standardize individual measurements

scholarly journals PSIV-17 Muscular Tension as a Marker of Stress in Horses

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 285-286
Author(s):  
Ellen M Rankins ◽  
Helio C Manso ◽  
Karyn Malinowski ◽  
Kenneth H McKeever
Keyword(s):  
Social Isolation ◽  
Repeated Measures ◽  
Mixed Model ◽  
Negative Binomial ◽  
Median Frequency ◽  
Novel Object ◽  
Emg Signal ◽  
Muscular Tension ◽  
Left And Right ◽  
The Right

Abstract Effects of social isolation, sham clipping, and novel object exposure on muscular and behavioral responses were investigated in horses. Humans increase muscular tension under psychologically or physically demanding situations and thus, similar conditions were expected to alter stress responses in horses. Eight mature Standardbreds (4 mares, 4 geldings) were exposed to 3 min of social isolation (ISO), sham clipping (CLIP), novel object (NOV), and control (CON) conditions in a replicated 4×4 Latin Square experimental design with 10 min washout periods. Surface electromyography (EMG) and stress-related behaviors were recorded continuously. Median frequency (MF) and average rectified value (ARV) of the EMG signal were calculated for the first, middle, and final 10 sec of each period. EMG data were log transformed prior to analysis with a mixed model, repeated measures ANOVA. Behavior data were analyzed using a negative binomial distribution mixed model ANOVA. Significantly different means were separated using Tukey’s method. More stress-related behaviors (P &lt; 0.05) were observed during ISO (3.25 ± 0.26, LSM ± SE) than CON (1.46 ± 0.29) or CLIP (1.50 ± 0.36). ISO tended (P = 0.054) to produce more stress-related behaviors than NOV (2.31 ± 0.28). CLIP and ISO produced higher (P &lt; 0.05) ARV than CON or NOV in the left and right masseter. CLIP elicited the highest (P &lt; 0.05) MF in the left and right masseter with ISO resulting in lower (P &lt; 0.05) MF than CON. ARV was higher (P &lt; 0.05) in the left cervical trapezius during ISO as compared to all other conditions and in the right as compared to CON and CLIP. In the right cervical trapezius, ISO elicited higher (P &lt; 0.05) MF than CON. Increased stress-related behaviors indicate a stress response was induced. Elevated muscular activity suggests muscular tension can be used in assessing stress.

scholarly journals Effect of Ivermectin Treatment on the Frequency of Seizures in Persons with Epilepsy Infected with Onchocerca volvulus

Pathogens ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Alfred Dusabimana ◽  
Solomon Tsebeni Wafula ◽  
Stephen Jada Raimon ◽  
Joseph Nelson Siewe Fodjo ◽  
Dan Bhwana ◽  
...  
Keyword(s):  
Seizure Frequency ◽  
Mixed Model ◽  
Negative Binomial ◽  
Democratic Republic Of Congo ◽  
Controlled Trial ◽  
Onchocerca Volvulus ◽  
Ivermectin Treatment ◽  
Study Sites ◽  
Skin Snips ◽  
Negative Binomial Mixed Model

A clinical trial performed in the Democratic Republic of Congo (DRC), among persons with epilepsy (PWE) infected with Onchocerca volvulus treated with anti-seizure medication suggested that ivermectin reduces the seizure frequency. We assessed the effect of ivermectin treatment on seizure frequency in PWE with and without anti-seizure medication in three onchocerciasis endemic areas (Maridi, South Sudan; Aketi, DRC; and Mahenge, Tanzania). Pre- and 3–5 months post-ivermectin microfilariae densities in skin snips and seizure frequency were assessed. After ivermectin, the median (IQR) percentage reduction in seizure frequency in the study sites ranged from 73.4% (26.0–90.0) to 100% (50.0–100.0). A negative binomial mixed model showed that ivermectin significantly reduced the seizure frequency, with a larger decrease in PWE with a high baseline seizure frequency. Mediation analysis showed that ivermectin reduced the seizure frequencies indirectly through reduction in microfilariae densities but also that ivermectin may have a direct anti-seizure effect. However, given the short half-life of ivermectin and the fact that ivermectin does not penetrate the healthy brain, such a direct anti-seizure effect is unlikely. A randomized controlled trial assessing the ivermectin effect in people infected with O. volvulus who are also PWE on a stable anti-seizure regimen may be needed to clarify the causal relationship between ivermectin and seizure frequency.

Multivariate ordinal random effects models including subject and group specific response style effects

2021 ◽  
pp. 1471082X2097803
Author(s):  
Gunther Schauberger ◽  
Gerhard Tutz
Keyword(s):  
Random Effects ◽  
Repeated Measurements ◽  
Response Style ◽  
Response Styles ◽  
Parameter Estimates ◽  
Specific Response ◽  
Random Effects Models ◽  
Explanatory Variables ◽  
Subject Specific

Common random effects models for repeated measurements account for the heterogeneity in the population by including subject-specific intercepts or variable effects. They do not account for the heterogeneity in answering tendencies. For ordinal responses in particular, the tendency to choose extreme or middle responses can vary in the population. Extended models are proposed that account for this type of heterogeneity. Location effects as well as the tendency to extreme or middle responses are modelled as functions of explanatory variables. It is demonstrated that ignoring response styles may affect the accuracy of parameter estimates. An example demonstrates the applicability of the method.

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