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Methods for confounding adjustment i...

The University of Chicago.

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Methods for confounding adjustment in time series data: Applications to short term effects of air pollution on respiratory health.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Methods for confounding adjustment in time series data: Applications to short term effects of air pollution on respiratory health./
作者:	Zibman, Chava.
面頁冊數:	104 p.
附註:	Adviser: Vanja Dukic.
Contained By:	Dissertation Abstracts International69-07B.
標題:	Health Sciences, Epidemiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3322663
ISBN:	9780549744559

Methods for confounding adjustment in time series data: Applications to short term effects of air pollution on respiratory health.
Zibman, Chava.

Methods for confounding adjustment in time series data: Applications to short term effects of air pollution on respiratory health. - 104 p.

Adviser: Vanja Dukic.

Thesis (Ph.D.)--The University of Chicago, 2008.

This work explores methods for confounding adjustment in time series models. First, we will offer an analysis of daily time-series of asthma-prescriptions counts for Chicago's Medicaid population (aggregated at the ZIP-code level), as a function of air pollution during four summers. We employ a Bayesian hierarchical semi-parametric Poisson regression model, a generalized additive model where adjustment for confounding is implemented via a time-varying smooth function, which accounts for the slowly varying unobserved confounders. This function is represented via a pre-specified number of spline bases. We specify the prior distribution for the variance of basis coefficients ("smoothness parameter") to control the amount of confounding adjustment. In this manner, we are also able to account for the uncertainty regarding how much confounding adjustment is performed.

ISBN: 9780549744559Subjects--Topical Terms:

1019544
Health Sciences, Epidemiology.

Methods for confounding adjustment in time series data: Applications to short term effects of air pollution on respiratory health.
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245 1 0 $a Methods for confounding adjustment in time series data: Applications to short term effects of air pollution on respiratory health.
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520 $a This work explores methods for confounding adjustment in time series models. First, we will offer an analysis of daily time-series of asthma-prescriptions counts for Chicago's Medicaid population (aggregated at the ZIP-code level), as a function of air pollution during four summers. We employ a Bayesian hierarchical semi-parametric Poisson regression model, a generalized additive model where adjustment for confounding is implemented via a time-varying smooth function, which accounts for the slowly varying unobserved confounders. This function is represented via a pre-specified number of spline bases. We specify the prior distribution for the variance of basis coefficients ("smoothness parameter") to control the amount of confounding adjustment. In this manner, we are also able to account for the uncertainty regarding how much confounding adjustment is performed.
520 $a Next, we develop a data-driven method for choosing a subset of trigonometric basis functions to be used for representation of the non-parametric (confounder) component. We seek to identify those bases which explain much of the variation in the response, but not the variation that is already explained by the predictor of interest. Our approach involves looking at the spectral representations of the observed variables to select the set of candidate trigonometric functions to be used to adjust for confounding. In this way, we can account for higher frequency confounding using fewer basis functions than what might be required using splines. High-frequency confounding techniques may be of particular relevance to air-pollution studies. This approach is studied in a series of simulation studies, where we compare several selection criteria for choosing the number of bases. We conclude by applying this method again to the asthma data. Analyses of the pollution and asthma data consistently fail to show any significant effects of ozone at least at the relatively low levels of pollution observed in Chicago.
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