Using Randomization in a Clinical Study
Randomized controlled trials (RCTs) are the gold standard for clinical research. In an RCT, the subjects are randomly allocated into treatment groups (in a parallel trial) or into treatment-sequence groups (in a crossover design).
Randomization provides several advantages:
It tends to eliminate selection bias — preferentially giving certain treatments to certain subjects (assigning a placebo to the less “likeable” subjects) — and confounding, where the treatment groups differ with respect to some characteristic that influences the outcome.
It permits the application of statistical methods to the analysis of the data.
It facilitates blinding. Blinding (also called masking) refers to concealing the identity of the treatment from subjects and researchers, and can be one of two types:
Single-blinding: The subjects don’t know what treatment they’re receiving, but the investigators do.
Double-blinding: Neither the subjects nor the investigators know which subjects are receiving which treatments.
Blinding eliminates bias resulting from the placebo effect, whereby subjects often respond favorably to any treatment (even a placebo), especially when the efficacy variables are subjective, such as pain level. Double-blinding also eliminates deliberate and subconscious bias in the investigator’s evaluation of a subject’s condition.
The simplest kind of randomization involves assigning each newly enrolled subject to a treatment group by the flip of a coin or a similar method. But simple randomization may produce an unbalanced pattern, like the one shown for a small study of 12 subjects and two treatments: Drug (D) and Placebo (P).
If you were hoping to have six subjects in each group, you won’t like having only three subjects receiving the drug and nine receiving the placebo, but unbalanced patterns like this arise quite often from 12 coin flips.
A better approach is to require six subjects in each group, but to shuffle those six Ds and six Ps around randomly as shown:
This arrangement is better (there are exactly six drug and six placebo subjects), but this particular random shuffle happens to assign more drugs to the earlier subjects and more placebos to the later subjects (again, bad luck of the draw). If these 12 subjects were enrolled over a period of five or six months, seasonal effects might be mistaken for treatment effects (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.
You can create simple and blocked randomization lists in Excel using the RAND built-in function to shuffle the assignments. You can also use the GraphPad web page to generate blocked randomization lists quickly and easily.