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Full-Text Articles in Physical Sciences and Mathematics
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 …
Multilevel Latent Class Models With Dirichlet Mixing Distribution, Chongzhi Di, Karen Bandeen-Roche
Multilevel Latent Class Models With Dirichlet Mixing Distribution, Chongzhi Di, Karen Bandeen-Roche
Johns Hopkins University, Dept. of Biostatistics Working Papers
Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when …
A Method For Visualizing Multivariate Time Series Data, Roger D. Peng
A Method For Visualizing Multivariate Time Series Data, Roger D. Peng
Johns Hopkins University, Dept. of Biostatistics Working Papers
Visualization and exploratory analysis is an important part of any data analysis and is made more challenging when the data are voluminous and high-dimensional. One such example is environmental monitoring data, which are often collected over time and at multiple locations, resulting in a geographically indexed multivariate time series. Financial data, although not necessarily containing a geographic component, present another source of high-volume multivariate time series data. We present the mvtsplot function which provides a method for visualizing multivariate time series data. We outline the basic design concepts and provide some examples of its usage by applying it to a …
Jointly Modeling Continuous And Binary Outcomes For Boolean Outcomes: An Application To Modeling Hypertension, Xianbin Li, Brian S. Caffo, Elizabeth Stuart
Jointly Modeling Continuous And Binary Outcomes For Boolean Outcomes: An Application To Modeling Hypertension, Xianbin Li, Brian S. Caffo, Elizabeth Stuart
Johns Hopkins University, Dept. of Biostatistics Working Papers
Binary outcomes defined by logical (Boolean) "and" or "or" operations on original continuous and discrete outcomes arise commonly in medical diagnoses and epidemiological research. In this manuscript,we consider applying the “or” operator to two continuous variables above a threshold and a binary variable, a setting that occurs frequently in the modeling of hypertension. Rather than modeling the resulting composite outcome defined by the logical operator, we present a method that models the original outcomes thus utilizing all information in the data, yet continues to yield conclusions on the composite scale. A stratified propensity score adjustment is proposed to account for …