scholarly journals Multivariable association discovery in population-scale meta-omics studies

2021 ◽  
Vol 17 (11) ◽  
pp. e1009442
Author(s):  
Himel Mallick ◽  
Ali Rahnavard ◽  
Lauren J. McIver ◽  
Siyuan Ma ◽  
Yancong Zhang ◽  
...  
Keyword(s):  
Microbial Community ◽  
Repeated Measures ◽  
Statistical Power ◽  
Large Scale ◽  
Human Microbiome ◽  
Repeated Measurements ◽  
Multiple Time ◽  
Data Types ◽  
Cross Sectional ◽  
Population Scale

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.

scholarly journals Multivariable Association Discovery in Population-scale Meta-omics Studies

2021 ◽  
Author(s):  
Himel Mallick ◽  
Ali Rahnavard ◽  
Lauren J. McIver ◽  
Siyuan Ma ◽  
Yancong Zhang ◽  
...  
Keyword(s):  
Microbial Community ◽  
Repeated Measures ◽  
Statistical Power ◽  
Large Scale ◽  
Linear Models ◽  
Repeated Measurements ◽  
Multiple Time ◽  
Data Types ◽  
Cross Sectional ◽  
Population Scale

AbstractIt is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g. counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel disease (IBD) across multiple time points and omics profiles.

scholarly journals Psychological factors shaping public responses to COVID-19 digital contact tracing technologies in Germany

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anastasia Kozyreva ◽  
Philipp Lorenz-Spreen ◽  
Stephan Lewandowsky ◽  
Paul M. Garrett ◽  
Stefan M. Herzog ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Psychological Factors ◽  
Data Privacy ◽  
Large Scale ◽  
New Technology ◽  
Contact Tracing ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
The Government ◽  
A Chain

AbstractThe COVID-19 pandemic has seen one of the first large-scale uses of digital contact tracing to track a chain of infection and contain the spread of a virus. The new technology has posed challenges both for governments aiming at high and effective uptake and for citizens weighing its benefits (e.g., protecting others’ health) against the potential risks (e.g., loss of data privacy). Our cross-sectional survey with repeated measures across four samples in Germany ($$N = 4357$$ N = 4357 ) focused on psychological factors contributing to the public adoption of digital contact tracing. We found that public acceptance of privacy-encroaching measures (e.g., granting the government emergency access to people’s medical records or location tracking data) decreased over the course of the pandemic. Intentions to use contact tracing apps—hypothetical ones or the Corona-Warn-App launched in Germany in June 2020—were high. Users and non-users of the Corona-Warn-App differed in their assessment of its risks and benefits, in their knowledge of the underlying technology, and in their reasons to download or not to download the app. Trust in the app’s perceived security and belief in its effectiveness emerged as psychological factors playing a key role in its adoption. We incorporate our findings into a behavioral framework for digital contact tracing and provide policy recommendations.

scholarly journals Simulation-Based Learning to Improve Athletic Trainers’ Knowledge of Exertional Sudden Death Conditions: A Pilot Study

2020 ◽  
Author(s):  
Zachary Winkelmann ◽  
Elizabeth Neil ◽  
Kenneth Games ◽  
Stacy Walker ◽  
Lindsey Eberman
Keyword(s):  
Pilot Study ◽  
Continuing Education ◽  
Professional Education ◽  
Repeated Measures ◽  
Large Scale ◽  
Athletic Trainers ◽  
Cross Sectional ◽  
Simulation Based ◽  
Post Test

Purpose: Continuing education for the practicing clinician typically involves reading peer-reviewed journals and attending professional conferences. These mechanisms do not allow for practice and real-time evaluation of healthcare skills. Simulation-based learning has been widely used in professional education yet is not common in the continued development of the clinician in their lifespan. Method: We used a cross-sectional, repeated measures pilot study. The participants included 11 athletic trainers (age=40±14 years; certified experience=17±14 years) that engaged in a multi-modal continuing professional development session that included a lecture, large-scale simulated learning experience, and debriefing session at a healthcare conference. The outcome measures included 1) a 6-item effectiveness tool to assess the overall program, 2) pre, post, and 6-month follow-up knowledge assessments, and 3) a 6-month follow-up qualitative viewpoint statement. Results: The participants rated the program as effective and useful. On the knowledge assessment, the participants scored an average of 74% on the pre-test and 87% on the post-test with an average change score of a 20.5% increase following the educational session. We identified a significant improvement (P=0.002) in the participants from pre-test to post-test, however a decay in the knowledge improvements from post-test to follow-up at six months (P=0.188) was noted. Conclusion: A multi-modal educational intervention was effective at improving knowledge immediately following the session. This study offers promise that continuing education through simulation may improve knowledge acquisition while serving as a catalyst for clinical practice behavior change.

scholarly journals The impact of pandemic-related worry on cognitive functioning and risk-taking

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260061
Author(s):  
Kevin da Silva Castanheira ◽  
Madeleine Sharp ◽  
A. Ross Otto
Keyword(s):  
Information Processing ◽  
Risk Taking ◽  
Repeated Measures ◽  
Large Scale ◽  
Information Processing Speed ◽  
Economic Risk ◽  
Current Crisis ◽  
Cross Sectional ◽  
Speed Of Information Processing ◽  
The Impact

Here, we sought to quantify the effects of experienced fear and worry, engendered by the COVID-19 pandemic, on both cognitive abilities—speed of information processing, task-set shifting, and proactive control—as well as economic risk-taking. Leveraging a repeated-measures cross-sectional design, we examined the performance of 1517 participants, collected during the early phase of the pandemic in the US (April–June 2020), finding that self-reported pandemic-related worry predicted deficits in information processing speed and maintenance of goal-related contextual information. In a classic economic risk-taking task, we observed that worried individuals’ choices were more sensitive to the described outcome probabilities of risky actions. Overall, these results elucidate the cognitive consequences of a large-scale, unpredictable, and uncontrollable stressor, which may in turn play an important role in individuals’ understanding of, and adherence to safety directives both in the current crisis and future public health emergencies.

scholarly journals Locked in permanent employment – longitudinal associations with depressive and functional somatic symptoms

2020 ◽  
Author(s):  
Pekka Virtanen ◽  
Anne Hammarström ◽  
Urban Janlert
Keyword(s):  
Mental Health ◽  
Repeated Measures ◽  
Somatic Symptoms ◽  
Poor Mental Health ◽  
Repeated Measurements ◽  
Occupational Class ◽  
Cross Sectional ◽  
Permanent Employment ◽  
Longitudinal Associations ◽  
Smooth Transitions

Abstract Background Rapidly changing industrial structures evidently increase individuals’ perceptions of not being in the preferred job, and also being ‘locked’ in the current post. The research on longitudinal associations of such locked-in situations with mental health is still scant, and there are controversial findings. The present study explored five hypotheses about mental health as a precedent and an outcome of different locked-in situations among permanently employed individuals. Methods Survey data on depressive and functional somatic symptoms from age 16 to 43 and on locked-in situations (permanently employed in non-preferred job) at age 30 and age 43 were collected from 479 participants of the Northern Swedish Cohort Study. Based on these two measurements, the locked-in history was classified as ‘never’ (not locked-in at both ages), ‘early’ (locked-in at age 30 only), ‘late’ (locked-in at age 42 only) and ‘long’ (locked-in at both ages). Analysis of variance for repeated measures was used to compare the changes in mental health of the four subcohorts defined by the locked-in history. Results Earlier evidence on the cross-sectional association between feeling locked-in and having poor mental health was confirmed. Longitudinal analyses revealed that those with poor baseline mental health at age 16 tend to get into a locked-in situation in the early middle age, that getting out of a locked-in situation in is associated with improving and getting into a locked-in situation is associated with worsening mental health, and that the worsening is more pronounced and the improvement less pronounced in white-collar than in blue-collar employees. Conclusions The findings clarify the bidirectional causal associations between locked-in situation and poor mental health, as well as the importance of common methods bias and social class in studying these associations. With respect to labour policy, locked-in perceptions could be reduced by means of labour legislation, collective agreements and human resource management that enable smooth transitions between workplaces and occupations. Key terms permanent employment; ANOVA for repeated measurements; occupational class

scholarly journals Alcohol consumption is associated with widespread changes in blood DNA methylation: analysis of cross-sectional and longitudinal data

2018 ◽  
Author(s):  
Pierre-Antoine Dugué ◽  
Rory Wilson ◽  
Benjamin Lehne ◽  
Harindra Jayasekara ◽  
Xiaochuan Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Alcohol Consumption ◽  
Large Scale ◽  
Alcohol Intake ◽  
Methylation Status ◽  
Repeated Measurements ◽  
Cross Sectional ◽  
Blood Samples ◽  
Melbourne Collaborative Cohort Study ◽  
Risk Of Disease

ABSTRACTBackground:DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart.Methods:Using the Illumina Infinium HumanMethylation450 BeadChip, DNA methylation measures were determined using baseline peripheral blood samples from 5,606 adult Melbourne Collaborative Cohort Study (MCCS) participants. For a subset of 1,088 of them, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models adjusted for batch effects and potential confounders. Independent data from the LOLIPOP (N=4,042) and KORA (N=1,662) cohorts were used to replicate associations discovered in the MCCS.Results:Cross-sectional analyses identified 1,414 CpGs associated with alcohol intake at P<10-7, 1,243 of which had not been reported previously. Of these 1,243 novel associations, 1,078 were replicated (P<0.05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated (P<0.05) 403 of 518 associations that had been reported previously. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1,414 CpGs, 530 were differentially methylated (P<0.05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1,414 cross-sectional associations.Conclusion:Our study indicates that, for middle-aged and older adults, alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with changes in alcohol consumption.

Number of Days Required to Estimate Habitual Vegetable Variety: A Cross-Sectional Analysis Using Dietary Records for 7 Consecutive Days

Nutrients ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 56
Author(s):  
Ryoko Kurisaki ◽  
Osamu Kushida
Keyword(s):  
Analysis Of Variance ◽  
Repeated Measures ◽  
Cross Sectional Study ◽  
Repeated Measurements ◽  
Cross Sectional ◽  
Repeated Measures Analysis ◽  
Dietary Record ◽  
Significant Difference ◽  
Dietary Records ◽  
Sectional Analysis

The aim of this cross-sectional study was to examine the number of days required to estimate habitual vegetable variety by conducting a multiday, dietary record. Sixty respondents from three groups in Japan (rural residents, general students, and nutrition students) participated in the study using a self-administered questionnaire in September 2018. To measure vegetable variety, the number of different vegetables consumed was extracted from the dietary records of seven consecutive days. Differences in the number of vegetables consumed and the capture proportion over seven consecutive days between groups were examined using repeated measures analysis of variance and one-way analysis of variance. The vegetable variety between each day was also compared using Pearson’s correlation coefficient. The vegetable variety based on dietary records for seven consecutive days confirmed the differences between groups by repeated measurements (p = 0.013). However, there was no significant difference among groups in the capture proportion per survey day based on seven consecutive days. Furthermore, there were significant correlations between the number of vegetables consumed over seven consecutive days and that consumed on two or more days (r > 0.50, p < 0.01) and especially three or more days in all groups (r > 0.70, p < 0.001). The present study suggested that a dietary survey over two or more days could provide an estimate of habitual vegetable variety.

scholarly journals Accessible, curated metagenomic data through ExperimentHub

2017 ◽  
Author(s):  
Edoardo Pasolli ◽  
Lucas Schiffer ◽  
Paolo Manghi ◽  
Audrey Renson ◽  
Valerie Obenchain ◽  
...  
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Disease Classification ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Data Types ◽  
Command Line Interface ◽  
Functional Profiles

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut “enterotypes”, and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

scholarly journals Measuring physical activity using accelerometry in 13–15-year-old adolescents: the importance of including non-wear activities

2011 ◽  
Vol 14 (12) ◽  
pp. 2124-2133 ◽  
Author(s):  
Femke De Meester ◽  
Ilse De Bourdeaudhuij ◽  
Benedicte Deforche ◽  
Charlene Ottevaere ◽  
Greet Cardon
Keyword(s):  
Physical Activity ◽  
Repeated Measures ◽  
Large Scale ◽  
Total Sample ◽  
Young Adolescents ◽  
Cross Sectional ◽  
Time Activity ◽  
Wear Time ◽  
Significant Difference ◽  
The Impact

AbstractObjectiveThe present study aimed to examine the impact of non-wear activities registered in diaries when using accelerometers to assess physical activity (PA) in young adolescents.DesignData arise from a large-scale cross-sectional study on PA. PA was objectively assessed using Actigraph™ accelerometers (Actigraph MTI, Manufacturing Technology Inc., Pensacola, FL, USA) during seven consecutive days. Non-wear time activity diaries were provided to register the activities for which the accelerometer was removed. After correction to deal with over-reporting, the registered minutes of PA were used to replace periods of non-wear time measured by the accelerometer.SettingBetween October 2008 and May 2009 adolescents were recruited by home visits in Ghent (Belgium).SubjectsYoung adolescents (n 513; 48·6 % boys) aged 13 to 15 years.ResultsOf the total sample, 49·9 % registered at least one activity of moderate to vigorous intensity in the non-wear time activity diary. More adolescents registered an activity performed on a weekday than on a weekend day and the registered mean number of minutes of moderate to vigorous PA were higher on weekend days. Repeated-measures (M)ANOVA tests revealed a significant difference between the mean minutes with and without non-wear activities for all PA intensities, regardless of adolescents’ socio-economic status or gender. More adolescents achieved the PA recommendations after inclusion of the non-wear activities irrespective of accelerometer thresholds used.ConclusionsThe collection of information regarding non-wear time by non-wear time activity diaries when using accelerometers in 13–15-year-old adolescents can lead to different PA outcomes at the individual level and therefore can improve the ability to accurately measure PA.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

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