The Essentials of Biostatistics for Physicians, Nurses, and Clinicians

Michael R. Chernick

• Publisher: Wiley
• Year: 2011
• ISBN:
  9781118071946
• Edition: 1
• Topic: Medicine
• Subtopic: Biostatistics

A fundamental and straightforward guide to using and understanding statistical concepts in medical research

Designed specifically for healthcare practitioners who need to understand basic biostatistics but do not have much time to spare, The Essentials of Biostatistics for Physicians, Nurses and Clinicians presents important statistical methods used in today's biomedical research and provides insight on their appropriate application. Rather than provide detailed mathematics for each of these methods, the book emphasizes what healthcare practitioners need to know to interpret and incorporate the latest biomedical research into their practices.

The author draws from his own experience developing and teaching biostatistics courses for physicians and nurses, offering a presentation that is non-technical and accessible. The book begins with a basic introduction to the relationship between biostatistics and medical research, asking the question "why study statistics?, " while also exploring the significance of statisitcal methods in medical literature and clinical trials research. Subsequent chapters explore key topics, including:

• Correlation, regression, and logistic regression
• Diagnostics
• Estimating means and proportions
• Normal distribution and the central limit theorem
• Sampling from populations
• Contingency tables
• Meta-analysis
• Nonparametric methods
• Survival analysis

Throughout the book, statistical methods that are often utilized in biomedical research are outlined, including repeated measures analysis of variance, hazard ratios, contingency tables, log rank tests, bioequivalence, cross-over designs, selection bias, and group sequential methods. Exercise sets at the end of each chapter allow readers to test their comprehension of the presented concepts and techniques.

The Essentials of Biostatistics for Physicians, Nurses, and Clinicians is an excellent reference for doctors, nurses, and other practicing clinicians in the fields of medicine, public health, pharmacy, and the life sciences who need to understand and apply statistical methods in their everyday work. It also serves as a suitable supplement for courses on biostatistics at the upper-undergraduate and graduate levels.

CHAPTER 1

The What, Why, and How of Biostatistics in Medical Research

1.1 DEFINITION OF STATISTICS AND BIOSTATISTICS

The Oxford Dictionary of Statistics (2002, p. 349) defines statistics as “The science of collecting, displaying, and analyzing data.” Statistics is important in any scientific endeavor. It also has a place in the hearts of fans of sports, particularly baseball. Roger Angel in his baseball book, Late Innings, says “Statistics are the food of love.”

Biostatistics is the branch of statistics that deals with biology, both experiments on plants, animals, and living cells, and controlled experiments on humans, called clinical trials. Statistics is classified by scientific discipline because in addition to many standard methods that are common to statistical problems in many fields, special methods have been developed primarily for certain disciplines. So to illustrate, in biostatistics, we study longitudinal data, missing data models, multiple testing, equivalence and noninferiority testing, relative risk and odds ratios, group sequential and adaptive designs, and survival analysis, because these types of data and methods arise in clinical trials and other medical studies. Engineering statistics considers tolerance intervals and design of experiments. Environmental statistics has a concentration in the analysis of spatial data, and so does geostatistics. Econometrics is the branch of statistics studied by economists, and deals a lot with forecasting and time series.

Statisticians are professionals trained in the collection, display, and analysis of data and the distribution theory that characterizes the variability of data. To become a good applied statistician, one needs to learn probability theory and the methods of statistical inference as developed by Sir Ronald A. Fisher, Jerzy Neyman, Sir Harold Jeffreys, Jimmie Savage, Bruno deFinetti, Harald Cramer, Will Feller, A. N. Kolmogorov, David Blackwell, Erich Lehmann, C. R. Rao, Karl and Egon Pearson, Abraham Wald, George Box, William Cochran, Fred Mosteller, Herman Chernoff, David Cox, and John Tukey in the twentieth century. These are some of the major developers of the foundations of probability and statistics. Of course, when selecting a list of famous contributors like this, many have been unintentionally omitted. In the late twentieth century and early twenty-first century, computer-intensive statistics arose, and a partial list of the leaders of that development are Brad Efron, Leo Brieman, David Freedman, Terry Speed, Jerry Friedman, David Siegmund, and T. L. Lai. In the area of biostatistics, we should mention Thomas Fleming, Stuart Pocock, Nathan Mantel, Peter Armitage, Shein-Chung Chow, Jen-pei Liu, and Gordon Lan. You will be introduced to these and other famous probabilists and statisticians in this book. An applied statistician must also become familiar with at least one scientific discipline in order to effectively consult with scientists in that field.

Statistics is its own discipline because it is much more than just a set of tools to analyze data. Although statistics requires the tools of probability, which are mathematical, it should not be thought of as a branch of mathematics. It is the appropriate way to summarize and analyze data when the data contains an element of uncertainty. This is very common when measurements are taken, since there is a degree of inaccuracy in every measurement. Statisticians develop mathematical models to describe the phenomena being studied. These models may describe such things as the time a bus will arrival at a scheduled stop, how long a person waits in line at a bank, the time until a patient dies or has a recurrence of a disease, or future prices of stocks, bonds, or gasoline.

Based on these models, the statistician develops methods of estimation or tests of hypotheses to solve certain problems related to the data. Because almost every experiment involves uncertainty, statistics is the scientific method for quantitative data analysis.

Yet in the public eye, statistics and statisticians do not have a great reputation. In the course of a college education, students in the health sciences, business, psychology, and sociology are all required to take an introductory statistics course. The comments most common from these students are “this is the most boring class I ever took” and “it was so difficult, that I couldn’t understand any of it.” This is the fault of the way the courses are taught and not the fault of the subject. An introductory statistics course can be much easier to understand and more useful to the student than, say, a course in abstract algebra, topology, and maybe even introductory calculus. Yet many people don’t view it that way.

Also, those not well trained in statistics may see articles in medicine that are contradictory but still make their case through the use of statistics. This causes many of us to say “You can prove anything with statistics.” Also, there is that famous quote attributed to Disraeli. “There are lies, damn lies and statistics.” In 1954, Darrell Huff wrote his still popular book, How to Lie with Statistics. Although the book shows how graphs and other methods can be used to distort the truth or twist it, the main point of the book is to get a better understanding of these methods so as not to be fooled by those who misuse them. Statisticians applying valid statistical methods will reach consistent conclusions. The data doesn’t lie. It is the people that manipulate the data that lie. Four books that provide valuable lessons about misusing statistics are Huff (1954), Campbell (1974), Best (2001), and Hand (2008).

1.2 WHY STUDY STATISTICS?

The question is really why should medical students, physicians, nurses, and clinicians study statistics? Our focus is on biostatistics and the students we want to introduce it to. One good reason to study statistics is to gain knowledge from data and use it appropriately. Another is to make sure that we are not to be fooled by the lies, distortions, and misuses in the media and even some medical journals. The medical journals now commonly require good statistical methods as part of a research paper, and the sophistication of the methods used is greater. So we learn statistics so that we know what makes sense when reading the medical literature, and in order to publish good research.

We also learn statistics so that we can provide intelligent answers to basic questions of a statistical nature. For many physicians and nurses, there is a fear of statistics. Perhaps this comes from hearing horror stories about statistics classes. It also may be that you have seen applications of statistics but did not understand it because you have no training. So this text is designed to help you conquer your fear of statistics. As you learn and gain confidence, you will see that it is logical and makes sense, and is not as hard as you first thought.

Major employers of statisticians are the pharmaceutical, biotechnology, and medical device companies. This is because the marketing of new drugs, biologics, and most medical devices must be approved by the U.S. Food and Drug Administration (FDA), and the FDA requires the manufacturers to demonstrate through the use of animal studies and controlled clinical trials the safety and effectiveness of their product. These studies must be conducted using valid statistical methods. So any medical investigator involved in clinical trials sponsored by one of these companies really needs to understand the design of the trial and the statistical implications of the design and the sample size requirements (i.e., number of patients need in the clinical trial). This requires at least one basic biostatistics course or good on-the-job training.

Because of uncontrolled variability in any experimental situation, statistics is necessary to organize the data and summarize it in a way so that signals (important phenomena) can be detected when corrupted by noise. Consequently, bench scientists as well as clinical researchers need some acquaintance with statistics. Most medical discoveries need to be demonstrated using statistical hypothesis testing or confidence interval estimation. This has increased in importance in the medical journals. Simple t-tests are not always appropriate. Analyses are getting much more sophisticated. Death and other time-to-event data require statistical survival analysis methods for comparison purposes.

Most scientific research requires statistical analysis. When Dr. Riffenburgh (author of the text Statistics in Medicine, 1999) is told by a physician “I’m too busy treating patients to do research,” he answers, “When you treat a patient, you have treated a patient. When you do research, you have treated ten thousand patients.”

In order to amplify these points, I will now provide five examples from my own experience in the medical device and pharmaceutical industries where a little knowledge of statistics would have made life easier for some of my coworkers.

In the first scenario, suppose you are the coordinator for a clinical trial on an ablation catheter. You are enrolling subjects at five sites. You want to add a new site to help speed up enrollment. The IRB for the new site must review and approve your protocol for the site to enter your study. A member of the IRB asks what stopping rule you use for safety. How do you respond? You don’t even know what a stopping rule is or even that the question is related to statistics! By taking this course, you will learn that statisticians construct stopping rules based upon accumulated data. In this case, there may be safety issues, and the stopping rule could be based on reaching a high number of adverse events. You won’t know all the details of the rule or why the statistician chose, it but you will at least know that the statistician is the person who should prepare the response for the IRB.

Our second example involves you as a regulatory affairs associate at a medical device company that just completed an ablation trial for a new catheter. You have submitted your premarket approval application (PMA). In the statistical section of the PMA, the statistician has provided statistical analysis regarding the safety and efficacy of your catheter in comparison to other marketed catheters. A reviewer at the FDA sent you a letter asking why Peto’s method was not used instead of Greenwood’s approximation. You do not know what these two methods are or how they apply.

From this course, you will learn about survival analysis. In studying the effectiveness of an ablation procedure, we not only want to know that the procedure stopped...