Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.
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.
Department of Mathematics and Statistics, American University, Washington, DC 20016, USA. malloy@american.edu
Elizabeth J
EJ
Malloy
Jeffrey S
JS
Morris
Sara D
SD
Adar
Helen
H
Suh
Diane R
DR
Gold
Brent A
BA
Coull
D000397:Air Pollution; D001499:Bayes Theorem; D006801:Humans; D007958:Leukocyte Count; D016014:Linear Models; D008954:Models, Biological; D052638:Particulate Matter / Q000009:adverse effects*
D052638:Particulate Matter