The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Marc N. Elliott - One of the best experts on this subject based on the ideXlab platform.
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Smoothing across time in repeated cross‐sectional Data
Statistics in medicine, 2011Co-Authors: J. R. Lockwood, Daniel F. Mccaffrey, Claude Messan Setodji, Marc N. ElliottAbstract:Repeated Cross-Sectional samples are common in national surveys of health like the National Health Interview Survey (NHIS). Because population health outcomes generally evolve slowly, pooling Data across years can improve the precision of current-year annual estimates of disease prevalence and other health outcomes. Pooling over time is particularly valuable in health disparities research, where outcomes for small groups are often of interest and pooling Data across groups would bias disparity estimates. State-space modeling and Kalman filtering are appealing choices for smoothing Data across time. However, filtering can be problematic when few time points are available, as is common with annual Cross-Sectional Data. Problems arise because filtering relies on estimated variance components, which can be biased and imprecise when estimated with small samples, especially when estimated in tandem with linear trends. We conduct a simulation study showing that even when trends and variance components are estimated poorly, smoothing with these estimates can improve the mean squared error (MSE) of estimated health states for multiple racial/ethnic groups when the variance components are estimated with the pooled sample. We consider frequentist estimators with no trends, one common trend across groups, and separate trends for every group, as well as shrinkage estimators of trends through a Bayesian model. We show that the Bayesian model offers the greatest improvement in MSE, and that Bayesian Information Criterion (BIC)-based model averaging of the frequentist estimators with different trend assumptions performs nearly as well. We present empirical examples using the NHIS Data. Copyright © 2011 John Wiley & Sons, Ltd.
Wu Xiaogang - One of the best experts on this subject based on the ideXlab platform.
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Technique Research of Surface Skinning Reconstruction with Cross-Sectional Data
Computer Engineering, 2006Co-Authors: Wu XiaogangAbstract:Aiming for the 3D surface reconstruction with precise Cross-Sectional Data,a surface skinning reconstruction method based on NURBS is proposed.The method produces a continuous NURBS surface,it improves the weakness that surface shape loses the true and calculation is unsteady when traditional methods of surface reconstruction is dealing with non-uniform 3-D scanned Data points.The method is verified in practical system.
Johan Ormel - One of the best experts on this subject based on the ideXlab platform.
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determinants of smoking status cross sectional Data on smoking initiation and cessation
European Journal of Public Health, 2005Co-Authors: Jeanne A M Van Loon, Marja Tijhuis, Paul G Surtees, Johan OrmelAbstract:Background: Cigarette smoking is known to increase the risk of chronic disease. Improved understanding of factors that contribute to smoking initiation and cessation may help to underpin strategies that lead to smoking behavior change. Methods: Cross-Sectional Data obtained from 11 967 men and women, aged 20-65 years, were used to study associations with smoking initiation and smoking cessation within the general population. Information on smoking habits, socio-demographic factors and psychosocial factors were collected through self-administered questionnaires. Multiple logistic regression analyses were undertaken by gender. Results: Adverse childhood experiences and personality characteristics (including extraversion, neuroticism and hostility) were found to be related to smoking initiation. Age, marital status and tobacco-related factors were consistently associated with smoking cessation. Older people, married persons and those who smoked more cigarettes per day had a higher likelihood of quitting, both for men and women. Conclusions: Smoking initiation was found to be associated with adverse childhood events and with measures of personality whereas smoking cessation was associated predominantly with socio-demographic and tobacco use-related factors.
J. R. Lockwood - One of the best experts on this subject based on the ideXlab platform.
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Smoothing across time in repeated cross‐sectional Data
Statistics in medicine, 2011Co-Authors: J. R. Lockwood, Daniel F. Mccaffrey, Claude Messan Setodji, Marc N. ElliottAbstract:Repeated Cross-Sectional samples are common in national surveys of health like the National Health Interview Survey (NHIS). Because population health outcomes generally evolve slowly, pooling Data across years can improve the precision of current-year annual estimates of disease prevalence and other health outcomes. Pooling over time is particularly valuable in health disparities research, where outcomes for small groups are often of interest and pooling Data across groups would bias disparity estimates. State-space modeling and Kalman filtering are appealing choices for smoothing Data across time. However, filtering can be problematic when few time points are available, as is common with annual Cross-Sectional Data. Problems arise because filtering relies on estimated variance components, which can be biased and imprecise when estimated with small samples, especially when estimated in tandem with linear trends. We conduct a simulation study showing that even when trends and variance components are estimated poorly, smoothing with these estimates can improve the mean squared error (MSE) of estimated health states for multiple racial/ethnic groups when the variance components are estimated with the pooled sample. We consider frequentist estimators with no trends, one common trend across groups, and separate trends for every group, as well as shrinkage estimators of trends through a Bayesian model. We show that the Bayesian model offers the greatest improvement in MSE, and that Bayesian Information Criterion (BIC)-based model averaging of the frequentist estimators with different trend assumptions performs nearly as well. We present empirical examples using the NHIS Data. Copyright © 2011 John Wiley & Sons, Ltd.
Brian Rocke - One of the best experts on this subject based on the ideXlab platform.
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Socioeconomic inequality in blood pressure and its determinants: Cross-Sectional Data from Trinidad and Tobago
Journal of human hypertension, 2003Co-Authors: Martin Gulliford, D Mahabir, Brian RockeAbstract:Socioeconomic inequality in blood pressure and its determinants: Cross-Sectional Data from Trinidad and Tobago
