Kerrie L. Mengersen – författare

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance.  This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software. This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise.This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interestStatistical Methods for Hospital Monitoring with R: Provides functions to perform quality improvement and infection management data analysis.Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events.Provides a summary of key non-statistical aspects of hospital safety and easy to use functions.Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoringCovers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.

Provides an accessible foundation to Bayesian analysis using real world modelsThis book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches.Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems.Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods.Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing.Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.