Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Epidemiology
Spatial Misalignment In Time Series Studies Of Air Pollution And Health Data, Roger D. Peng, Michelle L. Bell
Spatial Misalignment In Time Series Studies Of Air Pollution And Health Data, Roger D. Peng, Michelle L. Bell
Johns Hopkins University, Dept. of Biostatistics Working Papers
Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter. One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of …
An Overview Of Observational Sleep Research With Application To Sleep Stage Transitioning, Brian S. Caffo, B. Swihart, A. Laffan, C. Crainiceanu, N. Punjabi
An Overview Of Observational Sleep Research With Application To Sleep Stage Transitioning, Brian S. Caffo, B. Swihart, A. Laffan, C. Crainiceanu, N. Punjabi
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this manuscript we give an overview of observational sleep research with a particular emphasis on sleep stage transitions. Sleep states represent a categorization of sleep electroencephalogram behavior over the night. We postulate that the rate of transitioning between sleep states is an important predictor of health. This claim is evaluated by comparing subjects with sleep disordered breathing to matched controls.
Model Selection And Health Effect Estimation In Environmental Epidemiology, Francesca Dominici, Chi Wang, Ciprian Crainiceanu, Giovanni Parmigiani
Model Selection And Health Effect Estimation In Environmental Epidemiology, Francesca Dominici, Chi Wang, Ciprian Crainiceanu, Giovanni Parmigiani
Johns Hopkins University, Dept. of Biostatistics Working Papers
In air pollution epidemiology, improvements in statistical analysis tools can translate into significant scientific advances, because of the unfavorable signal-to-noise ratios, and large correlations between exposures and confounders. Therefore, the use of a novel model selection approach in identifying time windows of exposure to pollutants that lead to adverse health effects is important and welcome. However, previous literature has raised concerns about approaches that select a model based on a given data set, and then estimate health effects in the same data assuming that the chosen model is correct. Problems can be particularly severe when: 1) the sample size is …