Linked Life Data

20156988

Source:http://linkedlifedata.com/resource/pubmed/id/20156988

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-6-11
pubmed:abstractText
Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1-7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-10725286, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-10811568, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-12933509, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-14757714, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-14985553, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-14998750, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-15536419, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-15618077, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-15961590, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-16522832, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-16835049, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-17688488, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-17888041, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-18066097, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-18422792, http://linkedlifedata.com/resource/pubmed/commentcorrection/20156988-8179653
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1468-4357
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
11
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
432-52
pubmed:dateRevised
2011-8-1
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.
pubmed:affiliation
Department of Mathematics and Statistics, American University, Washington, DC 20016, USA. malloy@american.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural