Biostatistics for Epidemiology and Public Health Using R
A national network television newscast aired the following fi ve stories in 20061:
■ A report on the use of nontraditional, nonmainstream medicine, such as traditional Chinese medicine (TCM), to help people who have type 1 diabetes
■ A neighborhood’s concern about a sharp rise in the number of children with asthma and autism living in a western U.S. state
■ A report on the latest Centers for Disease Control and Prevention (CDC) recommendations regarding who should receive the fl u vaccine and when
■ A discussion of the extensive infectious disease monitoring strategy being implemented in a coastal city in the southern United States that was recently hit by a massive hurricane
■ A report on a study, published in a leading medical journal, of a likely association between an increased risk of cancer and workers’ exposure to a particular chemical
Each of these news stories included interviews with public health offi cials or investigators who called themselves epidemiologists.
Who are these epidemiologists, and what do they do? What is epidemiology? This book is intended to answer these questions. In doing so, it describes what epidemiology is, how it has evolved, how it is used today, and what some of its key methods and concepts are. The focus is on epidemiology in public health practice—that is, the kind of epidemiology that is done at local, state, and national health departments. Data analysis—the processing of information collected by observation or experimentation— is a very important part of epidemiologic investigations. Hence, the state of the art in epidemiologic studies is being steadily advanced as the capabilities and capacity of computing facilities and the computing environment in general move ahead. Today, epidemiologists around the world can choose from many commercially available and widely used biostatistical software packages.
A relatively new software package called R, developed in 1993 and freely available via the Internet, is the most promising. R has many advanced regression modeling functions, such as multilinear regression, logistic regression, survival analysis, and multilevel modeling. Supported as it is by leading biostatistical experts worldwide, R is now ubiquitous and provides everything that an epidemiologic data analyst needs.
The purpose of this book is to make R readily accessible, on a hands-on level, to all future epidemiologists for research, data processing, and presentation. This book is essentially about learning R with an emphasis on applications to epidemiology, public health, and preventive medicine. To make the best use of this text, readers should have some background in basic computer usage. With R and the supplied datasets, users should be able to work through each section, learning the techniques of data management, related biostatistical theories, and the practice of data analysis and graphical presentations. The book is systematically organized into seven chapters, each with a number of main sections covering the spectrum of applicable R codes for biostatistical applications in epidemiology and public health.
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