Cluster analysis of microbiome data by using mixtures of Dirichlet–multinomial regression models

2020 ◽  
Vol 69 (5) ◽  
pp. 1163-1187
Author(s):  
Sanjeena Subedi ◽  
Drew Neish ◽  
Stephen Bak ◽  
Zeny Feng
Keyword(s):  
Cluster Analysis ◽  
Regression Models ◽  
Multinomial Regression ◽  
Microbiome Data

scholarly journals Correction to: MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Matthew D. Koslovsky ◽  
Marina Vannucci
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
R Package ◽  
Bayesian Variable Selection ◽  
Multinomial Regression

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Uptake of Childcare Arrangements—Grandparental Availability and Availability of Formal Childcare

2021 ◽  
Vol 10 (2) ◽  
pp. 50
Author(s):  
Naomi Biegel ◽  
Karel Neels ◽  
Layla Van den Berg
Keyword(s):  
Decision Making ◽  
Informal Care ◽  
Regression Models ◽  
Local Level ◽  
Formal Care ◽  
Multinomial Regression ◽  
Contextual Data ◽  
Care Arrangement ◽  
Combined Use ◽  
Formal Childcare

Grandparents constitute an important source of childcare to many parents. Focusing on the Belgian context, this paper improves our understanding of childcare decision-making by investigating how formal childcare availability and availability of grandparents affect childcare arrangements. By means of multinomial regression models we simultaneously model uptake of formal and informal childcare by parents. Combining linked microdata from the Belgian censuses with contextual data on childcare at the level of municipalities, we consider formal childcare availability at a local level, while including a wide array of characteristics which may affect grandparental availability. Results indicate that increasing formal care crowds-out informal care as the sole care arrangement, whereas combined use of formal and informal care becomes more prevalent. Characteristics indicating a lack of grandmaternal availability increase uptake of formal care and inhibit to a lesser extent the uptake of combined formal and informal care. While increasing formal care substitutes informal care use, the lack of availability of informal care by grandparents may be problematic, particularly for those families most prone to use informal care.

scholarly journals Batch effects correction for microbiome data with Dirichlet-multinomial regression

2018 ◽  
Vol 35 (13) ◽  
pp. 2348-2348 ◽  
Author(s):  
Zhenwei Dai ◽  
Sunny H Wong ◽  
Jun Yu ◽  
Yingying Wei
Keyword(s):  
Batch Effects ◽  
Multinomial Regression ◽  
Microbiome Data

scholarly journals Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248956
Author(s):  
Elizabeth R. Lusczek ◽  
Nicholas E. Ingraham ◽  
Basil S. Karam ◽  
Jennifer Proper ◽  
Lianne Siegel ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Regression Models ◽  
Principal Component ◽  
Hospitalized Patients ◽  
Future Research ◽  
Ensemble Clustering ◽  
Multinomial Regression ◽  
Tailored Therapy ◽  
Clinical Phenotypes ◽  
Multivariable Models

Purpose Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. Methods This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes. Results The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11–17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10–6.00), p = 0.03) increases in hazard of death relative to phenotype III. Conclusion We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.

scholarly journals Nanomaterials’ Influence on the Performance of Thermal Insulating Mortars—A Statistical Analysis

2020 ◽  
Vol 10 (7) ◽  
pp. 2219 ◽  
Author(s):  
Marco Pedroso ◽  
Inês Flores-Colen ◽  
José Dinis Silvestre ◽  
Maria da Glória Gomes
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Regression Models ◽  
Production Efficiency ◽  
Bivariate Analysis ◽  
Insulating Materials ◽  
Thermal Insulating ◽  
Mechanical And Physical Properties ◽  
Overall Performance ◽  
Thermal Conductivities

This research provides a statistical analysis of the mechanical and physical properties of thermal insulating mortars developed in the laboratory and by the industry with and without the incorporation of nanomaterials. This was evaluated by carrying out a uni and bivariate analysis, principal components and factor analysis, cluster analysis, and the application of regression models. The results show that it is possible to find associations between these mortars’ properties, but also how these formulations’ development can be approached in the future to achieve better overall performance. They also show that the use of nanomaterials, namely silica aerogel, significantly improved the mortars’ thermal insulation capabilities, allowing us to obtain mortar formulations with thermal conductivities below the values presented by classic thermal insulating materials. Therefore, with this investigation, other researchers can support their product-development choices when incorporating nanomaterials to reduce mortars’ thermal conductivities, increasing their production efficiency, overall multifunctionality, and sustainability.

scholarly journals From margarine to butter: predictors of changing bread spread in an 11-year population follow-up

2015 ◽  
Vol 19 (9) ◽  
pp. 1707-1717
Author(s):  
Ritva Prättälä ◽  
Esko Levälahti ◽  
Tea Lallukka ◽  
Satu Männistö ◽  
Laura Paalanen ◽  
...  
Keyword(s):  
Vegetable Oil ◽  
Regression Models ◽  
Saturated Fat ◽  
Sharp Decrease ◽  
Health Behaviours ◽  
Multinomial Regression ◽  
Soft Margarine ◽  
Health Related ◽  
Representative Random Sample

AbstractObjectiveFinland is known for a sharp decrease in the intake of saturated fat and cardiovascular mortality. Since 2000, however, the consumption of butter-containing spreads – an important source of saturated fats – has increased. We examined social and health-related predictors of the increase among Finnish men and women.DesignAn 11-year population follow-up.SettingA representative random sample of adult Finns, invited to a health survey in 2000.SubjectsAltogether 5414 persons aged 30–64 years at baseline in 2000 were re-invited in 2011. Of men 1529 (59 %) and of women 1853 (66 %) answered the questions on bread spreads at both time points. Respondents reported the use of bread spreads by choosing one of the following alternatives: no fat, soft margarine, butter–vegetable oil mixture and butter, which were later categorized into margarine/no spread and butter/butter–vegetable oil mixture (= butter). The predictors included gender, age, marital status, education, employment status, place of residence, health behaviours, BMI and health. Multinomial regression models were fitted.ResultsOf the 2582 baseline margarine/no spread users, 24.6% shifted to butter. Only a few of the baseline sociodemographic or health-related determinants predicted the change. Finnish women were more likely to change to butter than men. Living with a spouse predicted the change among men.ConclusionsThe change from margarine to butter between 2000 and 2011 seemed not to be a matter of compliance with official nutrition recommendations. Further longitudinal studies on social, behavioural and motivational predictors of dietary changes are needed.

scholarly journals Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

Genes ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 104 ◽  
Author(s):  
Aeriel Belk ◽  
Zhenjiang Zech Xu ◽  
David O. Carter ◽  
Aaron Lynne ◽  
Sibyl Bucheli ◽  
...  
Keyword(s):  
Random Forest ◽  
Regression Models ◽  
Postmortem Interval ◽  
Random Forest Regression ◽  
Microbiome Data

scholarly journals Neighborhood Influences and BMI in Urban Older Adults

2015 ◽  
Vol 36 (6) ◽  
pp. 692-708 ◽  
Author(s):  
Arlesia L. Mathis ◽  
Ronica N. Rooks ◽  
Rima H. Tawk ◽  
Daniel J. Kruger
Keyword(s):  
Older Adults ◽  
Regression Models ◽  
Daily Life ◽  
Neighborhood Environment ◽  
Multinomial Regression ◽  
Neighborhood Crime ◽  
Neighborhood Influences ◽  
Perceived Neighborhood Environment ◽  
Very High

Objective: Increases in body weight and declining physical activity that may accompany aging are linked to a range of problems affecting daily life (i.e., decreased mobility and overall quality of life). This study investigates the actual and perceived neighborhood environment on overweight and obese urban older adults. Method: We selected 217 individuals aged 65+ who answered questions about their neighborhood on the 2009 Speak to Your Health survey. Using multinomial regression models and geospatial models, we examined relationships between neighborhood environment and BMI. Results: We found that obese older adults were 63% less likely to have a park within their neighborhood ( p = .04). Our results also show that older adults who perceive their neighborhood crime as very high are 12 times more likely to be overweight ( p = .04). Discussion: Findings suggest that parks may affect BMI in older adults; however, neighborhood perceptions play a greater role.

Agreement on diagnoses of mental health problems between an online clinical assignment and a routine clinical assignment

2013 ◽  
Vol 19 (2) ◽  
pp. 113-119 ◽  
Author(s):  
Per Håkan Bøsndbo ◽  
Børge Mathiassen ◽  
Monica Martinussen ◽  
Bjørn Helge Håndegard ◽  
Siv Kvernmo
Keyword(s):  
Mental Health ◽  
Mental Health Service ◽  
Health Service ◽  
Regression Models ◽  
Mental Health Problems ◽  
Well Being ◽  
Multinomial Regression ◽  
Diagnostic Categories ◽  
Adolescent Mental Health Service ◽  
Clinical Assignment

We examined the agreement between diagnoses assigned based on the Development and Well Being Assessment (DAWBA) information collected online, and ordinary day-to-day diagnostic assignment by Child and Adolescent Mental Health Service (CAMHS) clinicians. Diagnoses were compared for 286 patients. Raw agreement for diagnostic categories was 74-90%, resulting in kappa values of 0.41-0.49. Multinomial regression models for ‘emotional diagnosis’ and ‘hyperkinetic/conduct diagnosis’ were significant ( P < 0.001). Age, gender and number of informants significantly contributed to the explanation of agreement and disagreement. Agreement on mental health diagnoses may be sufficient to replace routine clinical assignment of diagnoses with an online clinical assignment, thereby saving time and resources.

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