Introductory Biostatistics

Chap T. Le, Lynn E. Eberly

• Editorial: Wiley
• Año: 2016
• ISBN:
  9781118596074
• Edición: 2
• Categoría: Medicine
• Categoría: Biostatistics

Maintaining the same accessible and hands-on presentation, Introductory Biostatistics, Second Edition continues to provide an organized introduction to basic statistical concepts commonly applied in research across the health sciences. With plenty of real-world examples, the new edition provides a practical, modern approach to the statistical topics found in the biomedical and public health fields.

Beginning with an overview of descriptive statistics in the health sciences, the book delivers topical coverage of probability models, parameter estimation, and hypothesis testing. Subsequently, the book focuses on more advanced topics with coverage of regression analysis, logistic regression, methods for count data, analysis of survival data, and designs for clinical trials. This extensive update of Introductory Biostatistics, Second Edition includes:

• A new chapter on the use of higher order Analysis of Variance (ANOVA) in factorial and block designs

• A new chapter on testing and inference methods for repeatedly measured outcomes including continuous, binary, and count outcomes

• R incorporated throughout along with SAS®, allowing readers to replicate results from presented examples with either software

• Multiple additional exercises, with partial solutions available to aid comprehension of crucial concepts

• Notes on Computations sections to provide further guidance on the use of software

• A related website that hosts the large data sets presented throughout the book

Introductory Biostatistics, Second Edition is an excellent textbook for upper-undergraduate and graduate students in introductory biostatistics courses. The book is also an ideal reference for applied statisticians working in the fields of public health, nursing, dentistry, and medicine.

1
DESCRIPTIVE METHODS FOR CATEGORICAL DATA

Most introductory textbooks in statistics and biostatistics start with methods for summarizing and presenting continuous data. We have decided, however, to adopt a different starting point because our focused areas are in the biomedical sciences, and health decisions are frequently based on proportions, ratios, or rates. In this first chapter we will see how these concepts appeal to common sense, and learn their meaning and uses.

1.1 PROPORTIONS

Many outcomes can be classified as belonging to one of two possible categories: presence and absence, nonwhite and white, male and female, improved and nonimproved. Of course, one of these two categories is usually identified as of primary interest: for example, presence in the presence and absence classification, nonwhite in the white and nonwhite classification. We can, in general, relabel the two outcome categories as positive (+) and negative (−). An outcome is positive if the primary category is observed and is negative if the other category is observed.

It is obvious that, in the summary to characterize observations made on a group of people, the number x of positive outcomes is not sufficient; the group size n, or total number of observations, should also be recorded. The number x tells us very little and becomes meaningful only after adjusting for the size n of the group; in other words, the two figures x and n are often combined into a statistic, called a proportion:

The term statistic means a summarized quantity from observed data. Clearly,

. This proportion p is sometimes expressed as a percentage and is calculated as follows:

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Example 1.1

A study published by the Urban Coalition of Minneapolis and the University of Minnesota Adolescent Healt...