A Bayesian approach to functional-based multilevel modeling of longitudinal data: applications to environmental epidemiology

Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90033-9987, USA.
Biostatistics (Impact Factor: 2.65). 04/2008; 9(4):686-99. DOI: 10.1093/biostatistics/kxm059
Source: PubMed


Flexible multilevel models are proposed to allow for cluster-specific smooth estimation of growth curves in a mixed-effects modeling format that includes subject-specific random effects on the growth parameters. Attention is then focused on models that examine between-cluster comparisons of the effects of an ecologic covariate of interest (e.g. air pollution) on nonlinear functionals of growth curves (e.g. maximum rate of growth). A Gibbs sampling approach is used to get posterior mean estimates of nonlinear functionals along with their uncertainty estimates. A second-stage ecologic random-effects model is used to examine the association between a covariate of interest (e.g. air pollution) and the nonlinear functionals. A unified estimation procedure is presented along with its computational and theoretical details. The models are motivated by, and illustrated with, lung function and air pollution data from the Southern California Children's Health Study.

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    • "We first incorporate a number of clinical covariates to model disease progression for each subject using a Bayesian hierarchical B-spline approach. Our methods are similar to those used in the development of longitudinal models for lung function in children observed in the presence of varying levels of ambient air pollution (Berhane and Molitor, 2008), although in our setting the response variable is an unobserved disease severity score rather than a measured quantity. From our estimated clinical trajectories , we can visualize and quantitatively compare the progression from exposure to latent infection in each of our subjects. "
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    ABSTRACT: Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis (Mtb) that affects millions of people worldwide. The majority of individuals who are exposed to Mtb develop latent infections, in which an immunological response to Mtb antigens is present but there is no clinical evidence of disease. Because currently available tests cannot differentiate latent individuals who are at low risk from those who are highly susceptible to developing active disease, there is considerable interest in the identification of diagnostic biomarkers that can predict reactivation of latent TB. We present results from our analysis of a controlled longitudinal experiment in which a group of rhesus macaques were exposed to a low dose of Mtb to study their progression to latent infection or active disease. Subsets of the animals were then euthanized at scheduled time points, and granulomas taken from their lungs were assayed for gene expression using microarrays. The clinical profiles associated with the animals following Mtb exposure revealed considerable variability, and we developed models for the disease trajectory for each subject using a Bayesian hierarchical B-spline approach. Disease severity estimates were derived from these fitted curves and included as covariates in linear models to identify genes significantly associated with disease progression. Our results demonstrate that the incorporation of clinical data increases the value of information extracted from the expression profiles and contributes to the identification of predictive biomarkers for TB susceptibility.
    Frontiers in Genetics 07/2014; 5. DOI:10.3389/fgene.2014.00240
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    • "A multilevel linear model was used that allowed for examination of the effects that risk factors have on attained BMI level at age 10 and the rate of growth during the follow-up period between the ages of 5–11 years [29,30]. This modeling approach properly adjusts for age- and sex- specific effects on BMI growth in children, provides an effective mechanism for assessing effects of risk factors on BMI level and growth, and also implicitly adjusts for baseline levels of BMI. "
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    ABSTRACT: Background Biologically plausible mechanisms link traffic-related air pollution to metabolic disorders and potentially to obesity. Here we sought to determine whether traffic density and traffic-related air pollution were positively associated with growth in body mass index (BMI = kg/m2) in children aged 5–11 years. Methods Participants were drawn from a prospective cohort of children who lived in 13 communities across Southern California (N = 4550). Children were enrolled while attending kindergarten and first grade and followed for 4 years, with height and weight measured annually. Dispersion models were used to estimate exposure to traffic-related air pollution. Multilevel models were used to estimate and test traffic density and traffic pollution related to BMI growth. Data were collected between 2002–2010 and analyzed in 2011–12. Results Traffic pollution was positively associated with growth in BMI and was robust to adjustment for many confounders. The effect size in the adjusted model indicated about a 13.6% increase in annual BMI growth when comparing the lowest to the highest tenth percentile of air pollution exposure, which resulted in an increase of nearly 0.4 BMI units on attained BMI at age 10. Traffic density also had a positive association with BMI growth, but this effect was less robust in multivariate models. Conclusions Traffic pollution was positively associated with growth in BMI in children aged 5–11 years. Traffic pollution may be controlled via emission restrictions; changes in land use that promote jobs-housing balance and use of public transit and hence reduce vehicle miles traveled; promotion of zero emissions vehicles; transit and car-sharing programs; or by limiting high pollution traffic, such as diesel trucks, from residential areas or places where children play outdoors, such as schools and parks. These measures may have beneficial effects in terms of reduced obesity formation in children.
    Environmental Health 06/2014; 13(1):49. DOI:10.1186/1476-069X-13-49 · 3.37 Impact Factor
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    ABSTRACT: The objective of this study is to examine the relationship between measured traffic density near the homes of children and attained body mass index (BMI) over an eight-year follow up. Children aged 9-10 years were enrolled across multiple communities in Southern California in 1993 and 1996 (n=3318). Children were followed until age 18 or high school graduation to collect longitudinal information, including annual height and weight measurements. Multilevel growth curve models were used to assess the association between BMI levels at age 18 and traffic around the home. For traffic within 150 m around the child's home, there were significant positive associations with attained BMI for both sexes at age 18. With the 300 m traffic buffer, associations for both male and female growth in BMI were positive, but significantly elevated only in females. These associations persisted even after controlling for numerous potential confounding variables. This analysis yields the first evidence of significant effects from traffic density on BMI levels at age 18 in a large cohort of children. Traffic is a pervasive exposure in most cities, and our results identify traffic as a major risk factor for the development of obesity in children.
    Preventive Medicine 10/2009; 50 Suppl 1:S50-8. DOI:10.1016/j.ypmed.2009.09.026 · 3.09 Impact Factor
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Sep 14, 2014