Innovative Statistical Methods for Public Health Data

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 UsefulAnalytical 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.

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• Probabilidade e Estatística
• Pesquisa médica
• Saude pública e medicina preventiva

Causal inference;Health surveillance;Incomplete or missing data;Public health statistics;Standardization and decomposition analysis (SDA);Statistics biomedical research