Suchergebnisse

1. Linear Regression -- 2. Introduction to Multi-Level Regression -- 3. Two-Level Multi-Level Modeling -- 4. Higher-Level Multi-Level Modeling -- 5. Longitudinal Data Analysis -- 6. Nonlinear Regression Modeling -- 7. Nonlinear Mixed-Effects Modeling -- 8. Generalized Linear Model -- 9. Generalized Multi-Level Model for Dichotomous Outcome -- 10. Generalized Multi-Level Model for Counts Outcome.

Intro -- Preface -- Contents -- Contributors -- Part I Modelling Clustered Data -- Methods for Analyzing Secondary Outcomes in Public Health Case-Control Studies -- 1 Introduction -- 2 Data Example: Genetic Association with Smoking Behavior in a Lung Cancer Case-Control Study -- 3 Design-Based Approach to Secondary Outcome Analysis -- 4 Model-Based Approach to Secondary Outcome Analysis -- 5 Data Analysis -- 5.1 Inverse Probability Weights -- 5.2 Propensity Score Matching -- 6 Discussion -- References -- Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems -- 1 Introduction -- 2 Background -- 3 Method -- 4 Results -- 4.1 Bivariate -- 4.1.1 Correlation Table Comparison -- 4.2 Multivariate -- 4.2.1 Path Comparison -- 5 Discussion -- References -- On the Inference of Partially Correlated Data with Applications to Public Health Issues -- 1 Introduction -- 2 Tests for Normal Data -- 2.1 Proposed Weighted Tests by Samawi and Vogel (2014) -- 2.2 New Test Procedure by Samawi and Vogel (2014) -- 2.3 Bootstrap Method to Estimate the p-Value of T0 in Case 2 and 3 -- 2.4 Illustration: A Vaginal Pessary Satisfaction Data (Samawi and Vogel 2014) -- 3 Tests for Binary Data -- 3.1 Test of Homogeneity in a Case-Control Study -- 3.2 Existing Methods for Testing the Above Hypotheses for Partially Matched Data -- 3.3 Proposed Method of Testing the Above Hypothesis for Partially Matched Data (Samawi and Vogel 2011) -- 3.4 Illustration and Final Results and Conclusions -- 3.4.1 Matched-Pairs Data Analysis -- 3.4.2 Unmatched Data Analysis (Excluding Matched-Pair Data) -- 3.4.3 Combined Tests of Matched-Pairs and Unmatched Data -- 4 Nonparametric Test for Partially Correlated Data -- 4.1 Combined Sign Tests for Correlated and Uncorrelated Data: Proposed Methods.

AbstractParents' use of spanking with infants and toddlers is of concern because this early onset of spanking may escalate over early childhood heightening the risk for child abuse. This study has two goals. First, using latent growth modelling, we examined the changes in parents' use of spanking across 6 years starting from infancy. Second, we identified predictors that can explain differences in parents' use of spanking. We analysed longitudinal data using a sample of 397 low‐income ethnic minority mothers (mean age = 24.53 years; 66% single) with infants/toddlers. Results showed that 40% of mothers endorsed spanking their infants and toddlers. Higher use of spanking was associated with younger mothers, African American mothers, and married/cohabiting mothers. The use of spanking was also found to increase across the first 6 years of childhood with steeper increase found among mothers with higher parental stress or with boys. Depressive symptoms were associated with higher spanking use at age 3. Given the high use of spanking and its trajectory of increase during early childhood, spanking at a young age could be a marker for elevated risk of maltreatment. Practice implications include expanding early childhood services to support parenting and to assess the psychological well‐being of mothers.

"This book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. However, the models in this book can be used to analyse any kind of data. The data are analysed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers' learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly available data, and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science, applied statistical analysts and scientists in public health, academic researchers, and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research"--

BACKGROUND : Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S. Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic. METHODS : We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted our model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was being conducted. With parameters obtained from this modeling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection. RESULTS : The data fit the model satisfactorily. The estimated daily growth rate was 16.8% overall with 95% CI: [15.95, 17.76%], suggesting a doubling period of 4 days. Based on the modeling result, the tipping point at which new cases will begin to decline will be on April 7th, 2020, with a peak of 32,860 new cases on that day. By the end of the epidemic, at least 792,548 (95% CI: [789,162, 795,934]) will be infected in the U.S. Based on our model, a total of 12,029 cases were not detected between January 22 (when the first case was detected in the U.S.) and April 4. CONCLUSIONS : Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented. Beyond informing public health decision-making, our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic. ; https://ghrp.biomedcentral.com ; hj2021 ...