Doing Calculations with the Greatest of Ease

For instructional purposes, some chapters in this book include step-by-step instructions for performing

statistical tests and analyses by hand. We include such instruction only to illustrate the concepts that

are involved in the procedure or to demonstrate calculations that are simple to do manually.

However, we demonstrate many of the statistical functions we talk about in this book using R, which is

a free, open-source software package. If you are in a class and assigned a particular software package

to use, you will have to use that software for the course, which may be commercial software

associated with a fee. However, if you are learning on your own, you may choose to use open-source

software, which is free. Chapter 4 provides guidance on both commercial and free software.

Concentrating on Epidemiologic Research

This book covers topics that are applicable to all areas of biostatistics, concentrating on

methods that are especially relevant to epidemiologic research — studies involving people. This

includes clinical trials, which are experiments done to develop therapeutic interventions such as

drugs. Because policy in healthcare is often based on the results from clinical trials, if you make

mistake analyzing clinical trial data, it can have disastrous and wide-ranging human and financial

consequences. Even if you don’t expect to ever work in a domain that relies heavily on clinical

trials (such as drug development research), ensuring that you have a working knowledge of how

to manage the statistical issues seen in clinical trials is critical.

Three chapters discuss clinical trials:

Chapter 5 describes the statistical aspects of clinical trials as three phases. First, it covers the

design phase, where a study protocol is written. Next, it describes the execution phase, where data

are collected, and efforts are made to prevent invalid or missing data. In the final phase, data from

the study are analyzed and interpreted to answer the hypotheses.

Chapter 7 presents epidemiologic study designs and explains the importance of the clinical trial as

a study design.

Chapter 20 explains the role well-designed clinical trials play in accruing evidence of causal

inference in biostatistics.

Much of the work in biostatistics is using data from samples to make inferences about the background

population from which the sample was drawn. Now that we have large databases, it is possible to

easily take samples of data. Chapter 6 provides guidance on different ways to take samples of larger

populations so you can make valid population-based estimates from these samples. Sampling is

especially important when doing observational studies. While clinical trials covered are experiments,

where participants are assigned interventions, in observational studies, participants are merely

observed, with data collected and statistics performed to make inferences. Chapter 7 describes these

observational study designs, and the statistical issues that need to be considered when analyzing data

arising from such studies.