This arrangement is better because there are exactly six participants assigned to drug and placebo

each. But this particular random shuffle happens to assign more drugs to the earlier participants and

more placebos to the later participant (which is just by chance). If the recruitment period is short, this

would be perfectly fine. However, if these 12 participants were enrolled over a period of five or six

months, seasonal effects may be mistaken for treatment effects, which is an example of confounding.

To make sure that both treatments are evenly spread across the entire recruitment period, you can use

blocked randomization, in which you divide your subjects into consecutive blocks and shuffle the

assignments within each block. Often the block size is set to twice the number of treatment groups. For

instance, a two-group study would use a block size of four. This is shown in Figure 5-3.

© John Wiley & Sons, Inc.

FIGURE 5-3: Blocked randomization.

You can create simple and blocked randomization lists in Microsoft Excel using the RAND()

built-in function to shuffle the assignments. You can also use the web page at

https://www.graphpad.com/quickcalcs/randomize1.cfm to generate blocked

randomization lists quickly and easily.

Selecting the analyses to use

You should select the appropriate analytic approach for each of your study hypotheses based on the

type of data involved, the structure of the study, and the requirements of the hypothesis. The rest of this

book describes statistical methods to analyze the kinds of data you’re likely to encounter in human

research. Your strategy is to apply them to a clinical trial design. In clinical trials, changes in values of

variables over time, and differences between treatments in crossover studies are often analyzed by

paired t tests and repeated-measures ANOVAs.

Differences between groups of participants in parallel studies are often analyzed by unpaired t tests

and ANOVAs. Often, final regression models are developed for clinical trial interpretation because

these can control for residual confounding (which are covered in the chapters in Part 5). In longer

clinical trials, time until death (survival time) and the times to the occurrence of other endpoint events

(besides death) are analyzed by survival methods (Part 6 focuses on survival analysis methods).

Determining how many participants to enroll in a clinical trial

Chapter 3 presents the concept of statistical power, and for a clinical trial, you should enroll enough

participants to provide sufficient statistical power when testing the primary objective of the study. The

specific way you calculate the required sample size depends on the statistical test that’s used for the

primary hypothesis. Each chapter of this book that describes hypothesis tests also shows how to

estimate the required sample size for that test. To get quick sample-size estimates, you can use

G*Power (an application for sample-size calculations described in Chapter 4), or you can use the