Applied Epidemiology Using R

Tom´as J. Arag´on Applied Epidemiology Using R September 27, 2010 Springer Berlin Heidelberg NewYork Hong Kong London Milan Paris Tokyo Your dedication goes here Preface We wrote this book to introduce R—a language and environment for statistical computing and graphics—to epidemiologists and health data analysts conducting epidemiologic studies. From our experience in public health practice, sometimes even formally trained epidemiologists lack the breadth of analytic skills required at health departments where resources are very limited. Recent graduates come prepared with a solid foundation in epidemiological and statistical concepts and principles and they are ready to run a multivariable analysis (which is not a bad thing we are grateful for highly trained staff). However, what is sometimes lacking is the practical knowledge, skills, and abilities to collect and process data from multiple sources (e.g., Census data; legally reportable diseases, death and birth registries) and to adequately implement new methods they did not learn in school. One approach to implementing new methods is to look for the “commands” among their favorite statistical packages (or to buy a new software program). If the commands do not exist, then the method may not be implemented. In a sense, they are looking for a custom-made solution that makes their work quick and easy. In contrast to custom-made tools or software packages, R is a suite of basic tools for statistical programming, analysis, and graphics. One will not find a “command” for a large number of analytic procedures one may want to execute. Instead, R is more like a set of high quality carpentry tools (hammer, saw, nails, and measuring tape) for tackling an infinite number of analytic problems, including those for which custom-made tools are not readily available or affordable. We like to think of R as a set of extensible tools to implement one’s analysis plan, regardless of simplicity or complexity. With practice, one not only learns to apply new methods, but one also develops a depth of understanding that sharpens one’s intuition and insight. With understanding comes clarity, focused problem-solving, and confidence. This book is divided into three parts. First, we cover how to process, manipulate, and operate on data in R. Most books cover this material briefly or leave it for an appendix. We decided to dedicate a significant amount of space to this topic with the assumption that the average epidemiologist is VIII Preface not familiar with R and a good grounding in the basics will make the later chapters more understandable. Second, we cover basic epidemiology topics addressed in most books but we infuse R to demonstrate concepts and to exercise your intuition. You may notice a heavier emphasis on descriptive epidemiology which is what is more commonly used at health departments, at least as a first step. In this section we do cover regression methods and graphical displays. Third, we have included “how to” chapters on a diversity of topics that come up in public health, such as meta-analysis, decision analysis, and multi-state modeling. Our goal is not to be comprehensive in each topic but to demonstrate how R can be used to implement a diversity of methods relevant to public health epidemiology and evidence-based practice. To help us spread the word, this book is available on the World Wide Web (http://www.medepi.com). We do not want financial or geographical barriers to limit access to this material. We are only presenting what we have learned from the generosity of others. Our hope is that more and more epidemiologists will embrace R for epidemiological applications, or at least, include it in their toolbox. Berkeley, California September 2010 Tom´ as Arag´ on Acknowledgements I would like to acknowledge