Bayesian modeling of time-varying and waning exposure effects.
In epidemiologic studies, there is often interest in assessing the association between exposure history and disease incidence. For many diseases, incidence may depend not only on cumulative exposure, but also on the ages at which exposure occurred. This article proposes a flexible Bayesian approach for modeling age-varying and waning exposure effects. The Cox model is generalized to allow the hazard of disease to depend on an integral, across the exposed ages, of a piecewise polynomial function of age, multiplied by an exponential decay term. Linearity properties of the model facilitate posterior computation via a Gibbs sampler, which generalizes previous algorithms for Cox regression with time-dependent covariates. The approach is illustrated by an application to the study of protective effects of breastfeeding on incidence of childhood asthma.
Dunson
David B
DB
Biostatistics Branch, MD A3-03, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709, USA. dunson1@niehs.nih.gov
Chulada
Patricia
P
Arbes
Samuel J
SJ
D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001249:Asthma / Q000453:epidemiology / Q000517:prevention & control; D001499:Bayes Theorem; D001942:Breast Feeding; D002648:Child; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure; D006801:Humans; D016016:Proportional Hazards Models; D013997:Time Factors