Dataset Information

Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study.

ABSTRACT: We present a case study illustrating the challenges of analyzing accelerometer data taken from a sample of children participating in an intervention study designed to increase physical activity. An accelerometer is a small device worn on the hip that records the minute-by-minute activity levels of the child throughout the day for each day it is worn. The resulting data are irregular functions characterized by many peaks representing short bursts of intense activity. We model these data using the wavelet-based functional mixed model. This approach incorporates multiple fixed effect and random effect functions of arbitrary form, the estimates of which are adaptively regularized using wavelet shrinkage. The method yields posterior samples for all functional quantities of the model, which can be used to perform various types of Bayesian inference and prediction. In our case study, a high proportion of the daily activity profiles are incomplete, i.e. have some portion of the profile missing, so cannot be directly modeled using the previously described method. We present a new method for stochastically imputing the missing data that allows us to incorporate these incomplete profiles in our analysis. Our approach borrows strength from both the observed measurements within the incomplete profiles and from other profiles, from the same child as well as other children with similar covariate levels, while appropriately propagating the uncertainty of the imputation throughout all subsequent inference. We apply this method to our case study, revealing some interesting insights into children's activity patterns. We point out some strengths and limitations of using this approach to analyze accelerometer data.

SUBMITTER: Morris JS

PROVIDER: S-EPMC2630189 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature

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Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study.

Morris Jeffrey S JS Arroyo Cassandra C Coull Brent A BA Ryan Louise M LM Herrick Richard R Gortmaker Steven L SL

Journal of the American Statistical Association 20061201 476

We present a case study illustrating the challenges of analyzing accelerometer data taken from a sample of children participating in an intervention study designed to increase physical activity. An accelerometer is a small device worn on the hip that records the minute-by-minute activity levels of the child throughout the day for each day it is worn. The resulting data are irregular functions characterized by many peaks representing short bursts of intense activity. We model these data using the ...[more]

PMID: 19169424

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Project description:MotivationDNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation.ResultsWe applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study.Availability and implementationAutomated software called WFMM is available at https://biostatistics.mdanderson.org/SoftwareDownload CpG Shore data is available at http://rafalab.dfci.harvard.edu NIH Roadmap Epigenomics data is available at http://compbio.mit.edu/roadmapSupplementary informationSupplementary data are available at Bioinformatics online.Contactjefmorris@mdanderson.org.

Project description:BackgroundAs no globally accepted dengue vaccines or specific antiviral therapies are currently available, controlling breeding sites of Aedes aegypti is a target to prevent dengue outbreaks. The present study aimed to characterize outdoor artificial breeding sites in urban households using an exhaustive classification system.MethodsA cross-sectional entomological survey was carried out in Colón city, Entre Ríos, Argentina, using a two-stage stratified sampling design during March and April 2014. The city was stratified given the degree of urbanization of each block, and blocks and households were randomly selected. All outdoor containers with water were inspected, and the presence of immature mosquitoes was recorded. Containers were classified according to physical, functional, and location attributes. Generalized linear mixed models were applied to take into account the aggregated nature of the data (containers in houses and houses in blocks).ResultsOverall, 207 houses were inspected. Out of 522 containers with water, 25% had immatures of Ae. aegypti (7336). In adjusted models, the abundance of immatures was higher in containers with increasing opening surface and volume, without roof cover, exposed to shadow, out of use or with functions related to gardening activities, household chores, water storage, or construction. At block level, immatures abundance was positively associated with the degree of urbanization.ConclusionsWe detected high immatures abundance in containers associated with water utilization. This suggests that containers involved in these activities, whether directly (e.g., water storage) or indirectly (e.g., incomplete water drainage in the last use), are susceptible to present a high immature abundance. Although our results indicate the importance of the type of use over the type of container, we encourage the use of both classification criteria for artificial breeding sites of mosquitoes, mainly because these are complementary. Additionally, generalized linear mixed models allowed us to analyse predictor variables at different scales (container/house/block) and consider the lack of independence between observations. An exhaustive analysis of artificial breeding sites that use this analytical methodology can lead to new information that could help designing more appropriate tools for dengue surveillance and control.

Dataset Information

Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study.

Publications

Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study.

Similar Datasets

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