Innovative Statistical Methods for Public Health Data

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Chen, Ding-Geng (Din) (Επιμελητής έκδοσης), Wilson, Jeffrey (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2015.
• Έκδοση: 1st ed. 2015.
• Σειρά: ICSA Book Series in Statistics,
• Θέματα: Statistics.; Laboratory medicine.; Public health.; Statistics for Life Sciences, Medicine, Health Sciences.; Public Health.; Laboratory Medicine.

 The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference, and it can be used in graduate level classes. 

 Part 1: Modelling Clustered Data -- Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies -- Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems -- On the Inference of Partially Correlated Data with Applications to Public Health Issues -- Modeling Time-Dependent Covariates in Longitudinal Data Analyses -- Solving Probabilistic Discrete Event Systems with Moore-Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data -- Part II: Modelling Incomplete or Missing Data -- On the Effects of Structural Zeros in Regression Models -- Modeling Based on Progressively Type-I Interval Censored Sample -- Techniques for Analyzing Incomplete Data in Public Health Research -- A Continuous Latent Factor Model for Non-ignorable Missing Data -- Part III: Healthcare Research Models -- Health Surveillance -- Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies -- Cusp Catastrophe Modeling in Medical and Health Research -- On Ranked Set Sampling Variation and its Applications to Public Health Research -- Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data -- Meta-analytic Methods for Public Health Research.