Over the past 13 years the Southern California Children's Health Study (CHS), one of the few long term? studies with focus on children's health, has demonstrated that children's respiratory health is adversely? affected by long term exposures to air pollution. It has also demonstrated the important role of genetics in? modulating the impact of the environment. These results have had significant policy implications for? protecting children's health, because they have added to our prior knowledge base, which had mainly? focused on adults. These results were made possible through the use of novel and innovative multi-level? modeling techniques, including many that were developed by CHS statisticians to address the various levels? of comparisons and challenging recurring issues that were necessitated by the scientific questions under? study. In this project, we continue to develop new statistical methods that are motivated by the general? theme of this renewal application towards integrated analysis of the exposure, lung function, asthma, genetic? and biomarker data. We propose to develop new statistical techniques in four interrelated areas. These? include (i) extension of our work on exposure modeling and measurement error, with renewed focus on? exploiting spatial correlations for intra-community variation and joint multivariate modeling of several? pollutants, (ii) development of techniques for analyzing the high volume of genetic data that will be generated? by Project 2, including methods for analyzing multiple single nucleotide polymorphisms (SNPs) per candidate? locus and multiple candidate loci per pathway, (iii) development of new latent variable based flexible multistate? modeling techniques that can handle joint analysis of multiple outcomes including continuous lung? function data and time-to-event asthma diagnosis and related phenotypes, giving us a tool to better? understand factors that potentially affect a spectrum of respiratory health outcomes in children in ways that? account for possible outcome misclassification, and (iv) development of novel techniques for integrated? analysis of the health outcome, genetic, biomarker and exposure data, where the integration of the scientific? evidence about the chronic effects of environmental exposure on children's health is examined by drawing? information from Projects 1 and 2. Although the topics we pursue will be motivated by CHS data and issues,? the methods we develop will also be applicable to a broad range of epidemiological studies.
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