Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel

Robert P. Hirsch

• Publisher: Wiley
• Year: 2016
• ISBN:
  9781119089995
• Edition: 1
• Topic: Medicine
• Subtopic: Biostatistics

A practical and methodological approach to the statistical logic of biostatistics in the field of health research

Focusing on a basic understanding of the methods and analyses in health research, Introduction to Biostatistical Applications in Health Research with Microsoft® Office Excel® provides statistical concepts for interpreting results using Excel. The book emphasizes the application of methods and presents the most common methodological procedures in health research, which includes multiple regression, ANOVA, ANCOVA, logistic regression, Cox regression, stratified analysis, life table analysis, and nonparametric parallels.

The book is constructed around a flowchart that outlines the appropriate circumstances for selecting a method to analyze a specific set of data. Beginning with an introduction to the foundational methods of statistical logic before moving on to more complex methods, Introduction to Biostatistical Applications in Health Research with Microsoft® Office Excel® also includes:

• Detailed discussions of how knowledge and skills in health research have been integrated with biostatistical methods
• Numerous examples with clear explanations that use mostly real-world health research data in order to provide a better understanding of the practical applications
• Implements Excel graphic representations throughout to help readers evaluate and analyze individual results
• An appendix with basic information on how to use Excel
• A companion website with additional Excel files, data sets, and homework problems as well as an Instructor's Solutions Manual

Introduction to Biostatistical Applications in Health Research with Microsoft® Office Excel® is an excellent textbook for upper-undergraduate and graduate-level courses in biostatistics and public health. In addition, the book is an appropriate reference for both health researchers and professionals.

Part One
Basic Concepts

Statisticians are used to dealing with uncertainty. Even so, there is one fact of which even statisticians are certain: the fact that we, as health researchers and practitioners, are always interested in applying the results of a study to persons, places, and/or times that were not included in the study. Our purpose is to interpret a study's observations as they relate to some larger group. In statistical terms, the larger group is called the population and the smaller group is called the sample. As we interpret health research data from a particular sample, it is always with the intention of using the sample's observations to draw some conclusion about the population from which that sample was taken.

Since we are always in the position of using a sample to understand the population, it is important that we take samples so that each one is representative of the population. Unfortunately, we do not know how to do that. To appreciate the problem, let us think about a population that we understand completely. For instance, suppose we were to think about a deck of 52 cards. In this “population” there are two characteristics of its members: suit and rank. These characteristics are distributed uniformly among the members of the population so that there are 13 cards in each of four suits and each suit has 13 ranks from deuce to ace. Now, thinking of that well-defined population, which five cards would you use to communicate the structure of the population?

If you feel frustrated with this question, it is because there is no completely correct answer. Any set of five cards fails to communicate precisely the structure of the deck. If we are unable to select a representative sample from such a well-defined population, imagine selecting a representative sample from a population that is really of interest to health researchers. It just cannot be done!

Even though we do not know how to make each sample representative of the population, we do know how to make a collection of samples representative of the population. We can do that with the deck of 52 cards by shuffling the deck and dealing several “samples” of five cards. In other words, we could let chance determine which members of the population are going to be included in each sample. Any particular hand of five cards might be distinctly unrepresentative of the deck, but if we continued to deal hands of five cards, in the long run those hands would represent the deck. We do the same thing when we take samples from populations as part of research. We let chance determine which members of the population end up in the sample.

It is this principle of random sampling that makes it necessary to understand the role of chance when we are interested in interpreting the results of health research. The primary purpose of statistics is to consider how chance influences that interpretation. To be interpreters of health research data, we need to recognize that the samples we examine might, just by chance, be substantially different from the rest of the popu...

Table of contents

• Cover
• Title Page
• Copyright
• Preface
• Acknowledgments
• Notices
• About the Companion Website
• Part One: Basic Concepts
• Part Two: Univariable Analyses
• Part Three: Bivariable Analyses
• Part Four: Multivariable Analyses
• Appendix A: Flowcharts
• Appendix B: Statistical Tables
• Appendix C: Standard Distributions
• Appendix D: Excel Primer
• Index
• End User License Agreement