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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Break down biostatistics, make sense of complex concepts, and pass your class
If you're taking biostatistics, you may need or want a little extra assistance as you make your way through. Biostatistics For Dummies follows a typical biostatistics course at the college level, helping you understand even the most difficult concepts, so you can get the grade you need. Start at the beginning by learning how to read and understand mathematical equations and conduct clinical research. Then, use your knowledge to analyze and graph your data. This new edition includes more example problems with step-by-step walkthroughs on how to use statistical software to analyze large datasets. Biostatistics For Dummies is your go-to guide for making sense of it all.
- Review basic statistics and decode mathematical equations
- Learn how to analyze and graph data from clinical research studies
- Look for relationships with correlation and regression
- Use software to properly analyze large datasets
Anyone studying in clinical science, public health, pharmaceutical sciences, chemistry, and epidemiology-related fields will want this book to get through that biostatistics course.
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Table of Contents
Cover
Title Page
Copyright
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Part 1: Getting Started with Biostatistics
Chapter 1: Biostatistics 101
Brushing Up on Math and Stats Basics
Doing Calculations with the Greatest of Ease
Concentrating on Epidemiologic Research
Drawing Conclusions from Your Data
A Matter of Life and Death: Working with Survival Data
Getting to Know Statistical Distributions
Figuring Out How Many Participants You Need
Chapter 2: Overcoming Mathophobia: Reading and Understanding Mathematical Expressions
Breaking Down the Basics of Mathematical Formulas
Focusing on Operations Found in Formulas
Counting on Collections of Numbers
Chapter 3: Getting Statistical: A Short Review of Basic Statistics
Taking a Chance on Probability
Some Random Thoughts about Randomness
Selecting Samples from Populations
Introducing Statistical Inference
Honing In on Hypothesis Testing
Going Outside the Norm with Nonparametric Statistics
Part 2: Examining Tools and Processes
Chapter 4: Counting on Statistical Software
Considering the Evolution of Statistical Software
Comparing Commercial to Open-Source Software
Checking Out Commercial Software
Focusing on Open-Source and Free Software
Choosing Between Code-based and Non–Code-Based Methods
Storing Data in the Cloud
Chapter 5: Conducting Clinical Research
Designing a Clinical Trial
Carrying Out a Clinical Trial
Analyzing Your Data
Chapter 6: Taking All Kinds of Samples
Making Forgivable (and Non-Forgivable) Errors
Framing Your Sample
Sampling for Success
Chapter 7: Having Designs on Study Design
Presenting the Study Design Hierarchy
Climbing the Evidence Pyramid
Part 3: Getting Down and Dirty with Data
Chapter 8: Getting Your Data into the Computer
Looking at Levels of Measurement
Classifying and Recording Different Kinds of Data
Checking Your Entered Data for Errors
Creating a File that Describes Your Data File
Chapter 9: Summarizing and Graphing Your Data
Summarizing and Graphing Categorical Data
Summarizing Numerical Data
Structuring Numerical Summaries into Descriptive Tables
Graphing Numerical Data
Chapter 10: Having Confidence in Your Results
Feeling Confident about Confidence Interval Basics
Calculating Confidence Intervals
Relating Confidence Intervals and Significance Testing
Part 4: Comparing Groups
Chapter 11: Comparing Average Values between Groups
Grasping Why Different Situations Need Different Tests
Using Statistical Tests for Comparing Averages
Estimating the Sample Size You Need for Comparing Averages
Chapter 12: Comparing Proportions and Analyzing Cross-Tabulations
Examining Two Variables with the Pearson Chi-Square Test
Focusing on the Fisher Exact Test
Calculating Power and Sample Size for Chi-Square and Fisher Exact Tests
Chapter 13: Taking a Closer Look at Fourfold Tables
Focusing on the Fundamentals of Fourfold Tables
Choosing the Correct Sampling Strategy
Producing Fourfold Tables in a Variety of Situations
Chapter 14: Analyzing Incidence and Prevalence Rates in Epidemiologic Data
Understanding Incidence and Prevalence
Analyzing Incidence Rates
Estimating the Required Sample Size
Part 5: Looking for Relationships with Correlation and Regression
Chapter 15: Introducing Correlation and Regression
Correlation: Estimating How Strongly Two Variables Are Associated
Regression: Discovering the Equation that Connects the Variables
Chapter 16: Getting Straight Talk on Straight-Line Regression
Knowing When to Use Straight-Line Regression
Understanding the Basics of Straight-Line Regression
Running a Straight-Line Regression
Interpreting the Output of Straight-Line Regression
Recognizing What Can Go Wrong with Straight-Line Regression
Calculating the Sample Size You Need
Chapter 17: More of a Good Thing: Multiple Regression
Understanding the Basics of Multiple Regression
Executing a Multiple Regression Analysis in Software
Interpreting the Output of a Multiple Regression Analysis
Watching Out for Special Situations that Arise in Multiple Regression
Calculating How Many Participants You Need
Chapter 18: A Yes-or-No Proposition: Logistic Regression
Using Logistic Regression
Understanding the Basics of Logistic Regression
Fitting a function with an S shape to your data
Running a Logistic Regression Model with Software
Interpreting the Output of Logistic Regression
Heads Up: Knowing What Can Go Wrong with Logistic Regression
Figuring Out the Sample Size You Need for Logistic Regression
Chapter 19: Other Useful Kinds of Regression
Analyzing Counts and Rates with Poisson Regression
Anything Goes with Nonlinear Regression
Smoothing Nonparametric Data with LOWESS
Chapter 20: Getting the Hint from Epidemiologic Inference
Staying Clearheaded about Confounding
Understanding Interaction (Effect Modification)
Getting Casual about Cause
Part 6: Analyzing Survival Data
Chapter 21: Summarizing and Graphing Survival Data
Understanding the Basics of Survival Data
Looking at the Life-Table Method
Heeding a Few Guidelines for Life-Tables and the Kaplan-Meier Method
Chapter 22: Comparing Survival Times
Comparing Survival between Two Groups with the Log-Rank Test
Considering More Complicated Comparisons
Estimating the Sample Size Needed for Survival Comparisons
Chapter 23: Survival Regression
Knowing When to Use Survival Regression
Grasping the Concepts behind Survival Regression
Executing a Survival Regression
Interpreting the Output of a Survival Regression
How Long Have I Got, Doc? Constructing Prognosis Curves
Estimating the Required Sample Size for a Survival Regression
Part 7: The Part of Tens
Chapter 24: Ten Distributions Worth Knowing
The Uniform Distribution
The Normal Distribution
The Log-Normal Distribution
The Binomial Distribution
The Poisson Distribution
The Exponential Distribution
The Weibull Distribution
The Student t Distribution
The Chi-Square Distribution
The Fisher F Distribution
Chapter 25: Ten Easy Ways to Estimate How Many Participants You Need
Comparing Means between Two Groups
Comparing Means among Three, Four, or Five Groups
Comparing Paired Values
Comparing Proportions between Two Groups
Testing for a Significant Correlation
Comparing Survival between Two Groups
Scaling from 80 Percent to Some Other Power
Scaling from 0.05 to Some Other Alpha Level
Adjusting for Unequal Group Sizes
Allowing for Attrition
Index
About the Authors
Connect with Dummies
End User License Agreement
List of Illustrations
Chapter 3
FIGURE 3-1: Distribution of number of private and public airports in 2011 in th...
FIGURE 3-2: The power of a statistical test increases as the sample size and th...
FIGURE 3-3: The power of a statistical test increases as the effect size increa...
FIGURE 3-4: Smaller effects need larger samples.
FIGURE 3-5: Skewed data (a) can sometimes be turned into normally distributed d...
Chapter 5
FIGURE 5-1: Simple randomization.
FIGURE 5-2: Random shuffling.
FIGURE 5-3: Blocked randomization.
Chapter 6
FIGURE 6-1: Example of multi-stage sampling from the National Health and Nutrit...
Chapter 7
FIGURE 7-1: Study design hierarchy.
FIGURE 7-2: Levels of evidence in study designs.
FIGURE 7-3: Ecologic study results.
FIGURE 7-4: 2x2 table cells.
FIGURE 7-5: Example of a typical case-control study 2x2 table.
FIGURE 7-6: Example of a typical cohort study 2x2 table.
Chapter 9
FIGURE 9-1: A frequency bar chart (a) and pie chart (b).
FIGURE 9-2: Four different shapes of distributions: normal (a), skewed (b), poi...
FIGURE 9-3: Distributions can be left-skewed (a), symmetric (b), or right-skewe...
FIGURE 9-4: Three distributions: leptokurtic (a), normal (b), and platykurtic (...
FIGURE 9-5: Population distribution of systolic blood pressure (SBP) measuremen...
FIGURE 9-6: Log-normal data are skewed (a), but the logarithms are normally dis...
FIGURE 9-7: Bar charts showing mean values (a) and standard deviations (b).
FIGURE 9-8: Box-and-whiskers charts: no-frills (a) and with variable width and ...
Chapter 12
FIGURE 12-1: The observed results comparing CBD to NSAIDs for the treatment of ...
FIGURE 12-2: Expected cell counts if the null hypothesis is true (there is no a...
FIGURE 12-3: Differences between observed and expected cell counts if the null ...
FIGURE 12-4: Differences between observed and expected cell counts.
FIGURE 12-5: Components of the chi-square statistic: squares of the scaled diff...
FIGURE 12-6: A general way of naming the cells of a cross-tab table.
Chapter 13
FIGURE 13-1: These designations for cell counts and totals are used throughout ...
FIGURE 13-2: A fourfold table summarizing obesity and hypertension in a sample ...
FIGURE 13-3: This is how data are summarized when evaluating a proposed new dia...
FIGURE 13-4: Results from a study of a new experimental home pregnancy test.
FIGURE 13-5: Comparing a treatment to a placebo.
FIGURE 13-6: Results of two raters reading the same set of 50 specimens and rat...
Chapter 15
FIGURE 15-1: 100 data points, with varying degrees of correlation.
FIGURE 15-2: Pearson
r
is based on a straight-line relationship.
Chapter 16
FIGURE 16-1: Straight-line regression is appropriate for both strong and weak l...
FIGURE 16-2: On average, a good-fitting line has smaller residuals than a bad-f...
FIGURE 16-3: Scatter plot of SBP versus body weight.
FIGURE 16-4: Sample straight-line regression output from R.
FIGURE 16-5: Scattergram of SBP versus weight, with the fitted straight line an...
FIGURE 16-6: The
residuals versus fitted
(a) and
normal
(b)
Q-Q
graphs help you...
Chapter 17
FIGURE 17-1: A scatter chart matrix for a set of variables prior to multiple re...
FIGURE 17-2: Output from multiple regression using the data from Table 17-2.
FIGURE 17-3: Diagnostic graphs from a regression.
FIGURE 17-4: Observed versus predicted outcomes for the model SBP ~ Age + Weigh...
Chapter 18
FIGURE 18-1: Dose versus mortality from Table 18-1: each individual’s data (a) ...
FIGURE 18-2: The first graph (a) shows the shape of the logistic function. The ...
FIGURE 18-3: The first graph (a) shows that when b is negative, the logistic fu...
FIGURE 18-4: Typical output from a logistic regression model. The output on the...
FIGURE 18-5: The logistic curve that fits the data from Table 18-1.
FIGURE 18-6: The classification table for the radiation example.
FIGURE 18-7: ROC curve from dose mortality data.
FIGURE 18-8: Visualizing the complete separation (or perfect predictor) problem...
Chapter 19
FIGURE 19-1: Yearly data on fatal highway accidents in one city.
FIGURE 19-2: Poisson regression output.
FIGURE 19-3: Poisson regression, assuming a constant increase in accident rate ...
FIGURE 19-4: Output from an exponential trend Poisson regression.
FIGURE 19-5: Linear and exponential trends fitted to accident data.
FIGURE 19-6: The blood concentration of an intravenous drug decreases over time...
FIGURE 19-7: Results of nonlinear regression in R.
FIGURE 19-8: Nonlinear model fitted to drug concentration data.
FIGURE 19-9: Nonlinear regression that estimates the PK parameters you want.
FIGURE 19-10: The relationship between age and hormone concentration doesn’t co...
FIGURE 19-11: The fitted LOWESS curve follows the shape of the data, whatever i...
FIGURE 19-12: You can adjust the smoothness of the fitted curve by adjusting th...
Chapter 20
FIGURE 20-1: Example of how confounders are associated with exposure and outcom...
Chapter 21
FIGURE 21-1: Survival of ten study participants following surgery for cancer.
FIGURE 21-2: Survival times from the date of surgery.
FIGURE 21-3: A partially completed life table to analyze the survival times sho...
FIGURE 21-4: Completed life table to analyze the survival times shown in Figure...
FIGURE 21-5: Hazard function (a) and survival function (b) results from life-ta...
FIGURE 21-6: Kaplan-Meier calculations.
FIGURE 21-7: Kaplan-Meier estimates of the hazard (a) and survival (b) function...
Chapter 22
FIGURE 22-1: Survival curves for two groups of laboratory animals.
FIGURE 22-2: A portion of the life-table calculations for two groups of laborat...
FIGURE 22-3: Basic log-rank calculations done manually (but please use software...
FIGURE 22-4: Proportional (a) and nonproportional (b) hazards relationships bet...
Chapter 23
FIGURE 23-1: Bending a straight line into different shapes by raising each poin...
FIGURE 23-2: Raising to a power works for survival curves, too.
FIGURE 23-3: Kaplan-Meier survival curves by treatment and clinical center.
FIGURE 23-4: Output of a PH regression from R.
FIGURE 23-5: Don’t try PH regression on this kind of data because it violates t...
FIGURE 23-6: Output of PH regression for generating prognostic curves.
Chapter 24
FIGURE 24-1: The uniform distribution.
FIGURE 24-2: The normal distribution at various means and standard deviations.
FIGURE 24-3: The log-normal distribution.
FIGURE 24-4: The binomial distribution.
FIGURE 24-5: The Poisson distribution.
FIGURE 24-6: The exponential distribution.
FIGURE 24-7: The Weibull distribution.
FIGURE 24-8: The Student t distribution.
FIGURE 24-9: The chi-square distribution.
FIGURE 24-10: The Fisher F distribution.
Guide
Cover
Table of Contents
Title Page
Copyright
Begin Reading
Index
About the Authors
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Biostatistics For Dummies®, 2nd Edition
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ISBN 978-1-394-25146-9 (pbk); ISBN 978-1-394-25147-6 (ebk); ISBN 978-1-394-25148-3 (ebk)
Introduction
Biostatistics is the practical application of statistical concepts and techniques to topics in the biology and life sciences fields. Because these are broad fields, biostatistics covers a very wide area. It is used when studying many types of experimental units, from viruses to trees to fleas to mice to people. Biostatistics involves designing research studies, safely conducting human research, collecting and verifying research data, summarizing and displaying the data, and analyzing the data to answer research hypotheses and draw meaningful conclusions.
It is not possible to cover all the subspecialties of biostatistics in one book, because such a book would have to include chapters on molecular biology, genetics, agricultural studies, animal research (both inside and outside the lab), clinical trials, and epidemiological research. So instead, we focus on the most widely applicable topics of biostatistics and on the topics that are most relevant to human research based on a survey of graduate-level biostatistics curricula from major universities.
About This Book
We wrote this book to be used as a reference. Our intention was for you to pull out this book when you want information about a particular topic. This means you don’t have to read it from beginning to end to find it useful. In fact, you can jump directly to any part that interests you. We hope you’ll be inclined to look through the book from time to time, open it to a page at random, read a page or two, and get a useful reminder or pick up a new fact.
Only in a few places does this book provide detailed steps about how to perform a particular statistical calculation by hand. Instruction like that may have been necessary in the mid-1900s. Back then, statistics students spent hours in a computing lab, which is a room that had an adding machine. Thankfully, we now have statistical software to do this for us (see Chapter 4 for advice on choosing statistical software). When describing statistical tests, our focus is always on the concepts behind the method, how to prepare your data for analysis, and how to interpret the results. We keep mathematical formulas and derivations to a minimum. We only include them when we think they help explain what’s going on. If you really want to see them, you can find them in many biostatistics textbooks, and they’re readily available online.
Because good study design is crucial for the success of any research, this book gives special attention to the design of both epidemiologic studies and clinical trials. We also pay special attention to providing advice on how to calculate the number of participants you need for your study. You will find easy-to-apply examples of sample-size calculations in the chapters describing significance tests in Parts 4, 5, and 6, and in Chapter 25.
Foolish Assumptions
We wrote this book to help several kinds of people. We assume you fall into one of the following categories:
Students at the undergraduate or graduate level who are taking a course in biostatistics and want help with the topics they’re studying in class
Professionals who have had no formal biostatistical training, and possibly no statistical training at all, who now must analyze biological or research data as part of their work
Doctors, nurses, and other healthcare professionals who want to carry out human research
If you’re interested in biostatistics, then you’re no dummy! But perhaps you sometimes feel like a dummy when it comes to biostatistics, or statistics in general, or even mathematics. Don’t feel bad. We both have felt that way many times over the years. In fact, we still feel like that whenever we are propelled into an area of biostatistics with which we are unfamiliar, because it is new to us. (If you haven’t taken a basic statistics course yet, you may want to get Statistics For Dummies by Deborah J. Rumsey, PhD — published by Wiley — and read parts of that book first.)
What is important to keep in mind when learning biostatistics is that you don’t have to be a math genius to be a good biostatistician. You also don’t need any special math skills to be an excellent research scientist who can intelligently design research studies, execute them well, collect and analyze data properly, and draw valid conclusions. You just have to have a solid grasp of the basic concepts and know how to utilize statistical software properly to obtain the output you need and interpret it.
Icons Used in This Book
Icons are the little graphics in the margins of this book, and are used to draw your attention to certain kinds of material. Here’s what they mean:
This icon signals information especially worth keeping in mind. Your main takeaways from this book should be the material marked with this icon.
We use this icon to flag explanations of technical topics, such as derivations and computational formulas that you don’t have to know to do biostatistics. They are included to give you deeper insight into the material.
This icon refers to helpful hints, ideas, shortcuts, and rules of thumb that you can use to save time or make a task easier. It also highlights different ways of thinking about a topic or concept.
This icon alerts you to discussion of a controversial topic, a concept that is often misunderstand, or a pitfall or common mistake to guard against in biostatistics.
Beyond the Book
In addition to the abundance of information and guidance related to using biostatistics for analysis of research data that we provide in this book, you get access to even more help and information online at Dummies.com. Check out this book’s online Cheat Sheet. Just go to www.dummies.com and search for “Biostatistics For Dummies Cheat Sheet.”
Where to Go from Here
You’re already off to a good start! You’ve read this introduction, so you have a good idea of what this book is all about. For a more detailed list of topics, take a look at the Contents at a Glance. This drills down into each part and shows you what each chapter is all about. Finally, skim through the full-blown Table of Contents, which drills further down into each chapter, showing you the headings for the sections and subsections of that chapter.
If you want to get the big picture of what biostatistics encompasses and the areas of biostatistics covered in this book, then read Chapter 1. This is a top-level overview of the book’s topics. Here are a few other special parts of this book you may want to jump into first, depending on your interest:
If you’re uncomfortable with mathematical notation, then
Chapter 2
is the place to start.
If you want a quick refresher on basic statistics like what you would learn in a typical introductory course, then read
Chapter 3
.
You can get an introduction to human research and clinical trials in
Chapters 5
,
7
, and
20
.
If you want to learn about collecting, summarizing, and graphing data, jump to
Part 3
.
If you need to know about working with survival data, you can go right to
Part 6
.
If you’re puzzled about a particular statistical distribution function, then look at
Chapter 24
.
And if you need to calculate some quick sample-size estimates, turn to
Chapter 25
.
Part 1
Getting Started with Biostatistics
IN THIS PART …
Get comfortable with mathematical notation that uses numbers, special constants, variables, and mathematical symbols — a must for all you mathophobes.
Review basic statistical concepts you may have learned previously, such as probability, randomness, populations, samples, statistical inference, and more.
