Introduction to Biostatistics with JMP

Steve Figard

Description

Explore biostatistics using JMP® in this refreshing introduction

Presented in an easy-to-understand way, Introduction to Biostatistics with JMP® introduces undergraduate students in the biological sciences to the most commonly used (and misused) statistical methods that they will need to analyze their experimental data using JMP. It covers many of the basic topics in statistics using biological examples for exercises so that the student biologists can see the relevance to future work in the problems addressed.

The book starts by teaching students how to become confident in executing the right analysis by thinking like a statistician then moves into the application of specific tests. Using the powerful capabilities of JMP, the book addresses problems requiring analysis by chi-square tests, t tests, ANOVA analysis, various regression models, DOE, and survival analysis. Topics of particular interest to the biological or health science field include odds ratios, relative risk, and survival analysis.

The author uses an engaging, conversational tone to explain concepts and keep readers interested in learning more. The book aims to create bioscientists who can competently incorporate statistics into their investigative toolkits to solve biological research questions as they arise.

Sujets

• COMPUTERS
• Enterprise software
• General officer
• Statistics
• Mathematics
• Biostatistics
• General
• JMP

Informations

• Publié par: SAS Institute
• Date de parution: 04 octobre 2019
• Nombre de lectures: 0
• EAN13: 9781635267181
• Langue: English
• Poids de l'ouvrage: 3 Mo

Extrait

The correct bibliographic citation for this manual is as follows: Figard, Steve. 2019. Introduction to Biostatistics with JMP ® . Cary, NC: SAS Institute Inc.
Introduction to Biostatistics with JMP ®
Copyright © 2019, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-64295-456-2 (Hardcover) ISBN 978-1-62960-633-0 (Paperback) ISBN 978-1-63526-720-4 (Web PDF) ISBN 978-1-63526-718-1 (epub) ISBN 978-1-63526-719-8 (kindle)
All Rights Reserved. Produced in the United States of America.
For a hard copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc.
For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication.
The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated.
U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). If FAR 52.227-19 is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Government’s rights in Software and documentation shall be only those set forth in this Agreement.
SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414
October 2019
SAS ® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.
Other brand and product names are trademarks of their respective companies.
SAS software may be provided with certain third-party software, including but not limited to open-source software, which is licensed under its applicable third-party software license agreement. For license information about third-party software distributed with SAS software, refer to http://support.sas.com/thirdpartylicenses .


About This Book
This book is based on the need for a college-level textbook with examples derived from the biological sciences as an introduction to biostatistics using JMP software. The contents of this book follow that of an introductory course on biostatistics created for and taught at Bob Jones University and reflects an intended audience of undergraduates forced to take a course they really rather wished they could avoid. These undergraduates generally have enough biological and mathematical knowledge to be dangerous, coupled with an innate fear of “Statistics,” that renders them quite dangerous indeed. Although a basic knowledge of math up to and including algebra is generally desirable, this work studiously avoids the underlying formulas and mathematical gyrations found in many of the more “comprehensive” books on statistics. This is by design. Most practitioners of statistics are not mathematicians, don’t care about the underlying math, and are content to let their software deal with those details as long as they can get the answers that they need with some assurance that they are correct. To that end, the emphasis here will be on how to set up and execute the statistical tests in JMP and how to interpret the output.
Before getting into the details of the individual tests, however, it is necessary to cover some basics of how to think like a statistician, or a reasonable facsimile thereof, to ensure that the right analysis is being done in the first place. The reader will find most of these preliminaries in the first chapters, with their application to specific tests being covered in the remaining chapters. The ultimate goal is to create bioscientists who can competently incorporate biostatistics into their investigative toolkits to solve biological research questions as they arise.
Why Am I Reading This Book?
Given the intended audience of this work – undergraduate biology and health science majors – the question asked in this section title is probably best answered by the opening paragraph of Dawn Hawkins’ excellent book when she writes:
Let us consider the likely scenario that you are a student of the biosciences. Whether you are a biomedic, a physiologist, a behaviourist, an ecologist, or whatever, you like learning about living things – you enjoy learning about the human body, bugs, and plants. Now, lo and behold, you have been forced to take a course that will make you do things with numbers and, dread-o-dread, even do something with numbers using a computer. You have probably decided that the people who are making you do this are mindless sadists. 1
This captures many of the expressions I see on the first day of class on all too many of my students. Lest this seems to be an exaggeration, I have been asking those students to write down a one-word description of how they feel about taking this course and collecting those
responses. Using the text explorer feature in JMP to create a word cloud, I have acquired the following words to date:

Note that the majority of students are “nervous,” “unsure,” “afraid” or “anxious” as opposed to “excited” or “intrigued.” (Although it is intriguing that so many use that word to describe a biostatistics course at all!)
But I will argue here that we are neither mindless nor sadists in our demand that you, as a promising practitioner of the biological sciences, learn how to “do statistics.”
There are at least three reasons why this is so. First, if you are going to be a scientist of any kind, you should have some understanding of the philosophy and history of science. This is the “big picture” into which you will orient your own efforts at contributing to the body of knowledge. The twentieth century saw a paradigm shift in the basic philosophy underlying the scientific enterprise. The prior century, in part due to successful efforts in astronomy at understanding the movement of planets and other heavenly bodies, had developed a philosophical determinism in which mathematic formulas led to precise predictions. As David Salsburg notes,
Science entered the nineteenth century with a firm philosophical vision that has been called the clockwork universe. It was believed that there were a small number of mathematical formulas (like Newton’s laws of motion and Boyle’s laws for gases) that could be used to describe reality and to predict future events. All that was needed for such prediction was a complete set of these formulas and a group of associated measurements that were taken with sufficient precision. 2
Alas and alack, the expected measurement precision never materialized. In fact, it proceeded to go from bad to worse. To account for this, scientists and mathematicians eventually developed and applied the ideas of randomness and probability to their observations, leading to a statistical model of reality that has revolutionized science. Salsburg points out, “Gradually, science began to work with a new paradigm, the statistical model of reality. By the end of the twentieth century, almost all of science had shifted to using statistical models.” 3 If you are going to be a scientist professionally, you should understand something of this underlying foundation on which you will build your own construct with your research.
Given that data analysis will almost certainly be needed to interpret the results of your experiments, the second reason for learning how to do statistics is simply to do so correctly. There is a need for practitioners of the art, which is not the same as theoreticians, who can do so with competence. This is particularly true for the clinical and biomedical community where the use and interpretation of biostatistics often guides therapy, human health, and public policy, and is critical to understand published research. As one of the best standard textbooks on molecular biology points out:
Statistics – the mathematics of probabilistic processes and noisy data-sets – is an inescapable part of every biologist’s life.
This is true in two main ways. First, imperfect measurement devices and other errors generate experimental noise in our data. Second, all cell-biological processes depend on the stochastic behavior of individual molecules …and this results in biological noise in our results. How, in the face of all this noise, do we come to conclusions about the truth of hypotheses? The answer is statistical analysis, which shows how to move from one level of description to another: from a set of erratic individual data points to a simpler description of the key features of the data. 4
And the demand for proficiency goes beyond the research laboratory. Clinical and medical testing laboratory professionals likewise need to be conversant with data analysis, as a recent article in Clinical Laboratory News observes: