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Activity Number: 377
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #310490
Title: A review of the Integrated Nested Laplace Approximation with Application to the Spatial Analysis of Gestational Age in British Columbia
Author(s): Priya Grewal*+
Companies:
Keywords:
Abstract:

Small for gestational age (SGA) is defined by the March of Dimes Birth Defects Foundation as ``babies who are smaller than most other babies of the same gestational age'' . To quantify this, SGA babies are defined as those babies that are below the 10th percentile for their weight and gestational age. These babies can still be fully healthy. However, low birthweight could have been the result of slowed or stopped growth in the uterus and signify something more serious. Birth weight and gestational age are key determinants in infant death and in diagnosing disability among newborn infants Our goal is to use a spatial Bayesian approach for examining individual and regional factors associated with babies being born small for their gestational age, and to develop maps depicting residual spatial patterns. To analyze our data we use a generalized linear mixed model including a spatial random effect to accommodate spatial clustering in the binary response variables. Typically, Markov chain Monte Carlo (MCMC) is used to fit Bayesian spatial random effects models. In our analysis, the size of our data and the slow convergence of the sampled Markov chain made this standard approach prohibitive. As an alternative, we investigate a relatively new approach, integrated nested Laplace approximations (INLA) as a more efficient alternative.


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