Applied Biostatistics for the Health Sciences - Richard J. Rossi

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large number of exercises have been included in each chapter, and the solutions to most of the exercises have also been included at the end of this text. I believe that the exercises accompanying this text do indeed cover a wide range of topics and levels of difficulty; there are many questions that deal with the conceptual aspects of the material discussed in each chapter that often lead to excellent classroom discussions. I believe that the successful completion of these exercises will help the student understand the statistical methods introduced in each chapter and gain the confidence necessary to be successful in upper division applied statistics courses, which, of course, are the goals of this textbook.

      Teaching from this Book

      In teaching a one-semester introductory biostatistics course from this textbook it is possible to cover most of the Chapters 1–7; for a two-semester sequence most of the Chapters 1–7 can be covered in the first semester, and Chapters 8–13 can be covered in the second semester. While I have not taught a course from this book on the quarter system, I believe that by carefully selecting the topics to be covered in a single quarter most of Chapters 1–7 can be covered; for a two-quarter sequence, most of the material in Chapters 1–7 could be covered in the first quarter, with the remainder of the book covered in the second quarter. However, there are many different ways to teach from this book, and I leave that to the discretion and goals of the instructor.

      Website

       http://www.mtech.edu/clsps/math/FacultyLinks/rossi_book.htm

      This website contains several of the data sets used in the examples and exercises included in Applied Biostatistics for the Health Sciences. The data sets are given in three formats, namely, as MINITAB worksheets, Microsoft Office Excel files, and text files. The website also contains several Microsoft Office Excel files that the students can use to simplify some of the computational aspects associated with sample sizes, confidence intervals, and p-values for Z and t-tests; these Microsoft Office Excel files are read only files that can be downloaded as needed for use by the instructor and the students.

      Acknowledgments

      I would like to thank the following statisticians who motivated me to write this book: Ray Carroll, David Ruppert, Jay Devore, Roxy Peck, Fred Ramsey, Dan Schafer, and Chip Todd. I would also like to thank Lloyd Gavin and H. Dan Brunk, two very inspirational advisors from whom I learned so much, my editors Susanne Steitz-Filler, Kimberly Monroe-Hill, and Christy Michael for their help and guidance, and my family and friends for their support in this endeavor.

      R. J. Rossi

       Butte, Montana

      PRIOR TO the twentieth century, medical research was primarily based on trial and error and empirical evidence. Diseases and the risk factors associated with a disease were not well understood. Drugs and treatments for treating diseases were generally untested. The rapid scientific breakthroughs and technological advances that took place in the latter half of the twentieth century have provided the modern tools and methods that are now being used in the study of the causes of diseases, the development and testing of new drugs and treatments, and the study of human genetics and have been instrumental in eradicating some infectious diseases.

      Modern biomedical research is evidence-based research that relies on the scientific method, and in many biomedical studies it is the scientific method that guides the formulation of well-defined research hypotheses, the collection of data through experiments and observation, and the honest analysis whether the observed data support the research hypotheses. When the data in a biomedical study support a research hypothesis, the research hypothesis becomes a theory; however, when data do not support a research hypothesis, new hypotheses are generally developed and tested. Furthermore, because statistics is the science of collecting, analyzing, and interpreting data, statistics plays a very important role in medical research today. In fact, one of the fastest growing areas of statistical research is the development of specialized data collection and analysis methods for biomedical and healthcare data. The science of collecting, analyzing, and interpreting biomedical and healthcare data is called biostatistics.

      1.1 What is Biostatistics?

      Biostatistics is the area of statistics that covers and provides the specialized methodology for collecting and analyzing biomedical and healthcare data. In general, the purpose of using biostatistics is to gather data that can be used to provide honest information about unanswered biomedical questions. In particular, biostatistics is used to differentiate between chance occurrences and possible causal associations, for identifying and estimating the effects of risk factors, for identifying the causes or predispositions related to diseases, for estimating the incidence and prevalence of diseases, for testing and evaluating the efficacy of new drugs or treatments, and for exploring and describing the well being of the general public.

      Biostatisticians commonly participate in research in the biomedical fields such as epidemiology, toxicology, nutrition, and genetics, and also often work for pharmaceutical companies. In fact, biostatisticians are widely employed in government agencies such as the National Institutes of Health (NIH), the Centers for Disease Control and Prevention (CDC), the Food and Drug Administration (FDA), and the Environmental Protection Agency (EPA). Biostatisticians are also employed by pharmaceutical companies, medical research units such as the MAYO Clinic and Fred Hutchison Cancer Research Center, Sloan-Kettering Institute, and many research universities. Furthermore, some biostatisticians serve on the editorial boards of medical journals and many serve as referees for biomedical journal articles in an effort to ensure the quality and integrity of data-based biomedical results that are published.

      1.2 Populations, Samples, and Statistics

      In every biomedical study there will be research questions to define the particular population that is being studied. The population that is being studied is called the target population. The target population must be a well-defined population so that it is possible to collect representative data that can be used to provide information about the answers to the research questions. Finding the actual answer to a research question requires that the entire target population be observed, which is usually impractical or impossible. Thus, because it is generally impractical to observe the entire target population, biomedical researchers will use only a subset of the population units in their research study. A subset of the population is called a sample, and a sample may provide information about the answer to a research question but cannot

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