B0711
Title: A latent functional approach for modeling multi-dimensional biomarker exposures on disease risk prediction
Authors: Paul Albert - National Cancer Institute (United States) [presenting]
Sung Duk Kim - National Cancer Institute (United States)
Abstract: Understanding the relationships between biomarkers of exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Importantly, these effects can be highly non-linear and can be in different directions. We develop a latent functional approach, which assumes that the individual joint effects of each biomarker exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.
