68 PART 2 Examining Tools and Processes

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 anal-

ysis 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 formulas, tables, and charts in

Chapter 25 and on the book’s Cheat Sheet at www.dummies.com (just search for

“Biostatistics For Dummies Cheat Sheet”).

You must also allow some extra space in your target sample-size estimate for

some of the enrolled participants to drop out or otherwise not contribute the data

you need for your analysis. For example, suppose that you need full data from

64 participants for sufficient statistical power to answer your main objective. If

you expect a 15 percent attrition rate from the study, which means you expect only

85 percent of the enrolled participants to have analyzable data, then you need

to plan to enroll 64/0.84, or 76, participants in the study.

Assembling the study protocol

A study protocol (or just protocol) is a document that lays out exactly what you plan

to do to collect and analyze data in a research study. For ethical reasons, every

research study involving human participants should have a protocol, and for other

types of studies, having a protocol prepared before starting the research is