Question

What is an example of a study that uses block randomization?

Answer 1

Randomized Block Design. With a randomized block design, the experimenter divides subjects into subgroups called blocks, such that the variability within blocks is less than the variability between blocks. Then, subjects within each block are randomly assigned to treatment conditions.

This function randomizes n individuals into k treatments, in blocks of size m.

Randomization reduces opportunities for bias and confounding in experimental designs, and leads to treatment groups which are random samples of the population sampled, thus helping to meet assumptions of subsequent statistical analysis (Bland, 2000).

Random allocation can be made in blocks in order to keep the sizes of treatment groups similar. In order to do this you must specify a sample size that is divisible by the block size you choose. In turn you must choose a block size that is divisible by the number of treatment groups you specify.

An advantage of small block sizes is that treatment group sizes are very similar. A disadvantage of small block sizes is that it is possible to guess some allocations, thus reducing blinding in the trial. An alternative to using large block sizes is to use random sequences of block sizes, which can be done in StatsDirect by specifying a block size of zero. The random block size option selects block sizes of 2, 3, or 4 (at random) times the number of treatments.

The randomization proceeds by allocating random permutations of treatments within each block.

Random allocation in blocks

Randomized with seed: 10

Subjects: 20

Block size: random between 4 and 8

Treatments: 2

Subject Treatment

1 A

2 B

3 A

4 B

5 B

6 A

7 A

8 B

9 A

10 A

11 B

12 B

13 B

14 A

15 A

16 B

17 A

18 B

19 A

20 B

Randomized Block Design

With a randomized block design, the experimenter divides subjects into subgroups called blocks, such that the variability within blocks is less than the variability between blocks. Then, subjects within each block are randomly assigned to treatment conditions. Compared to a completely randomized design, this design reduces variability within treatment conditions and potential confounding, producing a better estimate of treatment effects.

The table below shows a randomized block design for a hypothetical medical experiment.

Gender Treatment

Placebo Vaccine

Male 250 250

Female 250 250

Subjects are assigned to blocks, based on gender. Then, within each block, subjects are randomly assigned to treatments (either a placebo or a cold vaccine). For this design, 250 men get the placebo, 250 men get the vaccine, 250 women get the placebo, and 250 women get the vaccine.

It is known that men and women are physiologically different and react differently to medication. This design ensures that each treatment condition has an equal proportion of men and women. As a result, differences between treatment conditions cannot be attributed to gender. This randomized block design removes gender as a potential source of variability and as a potential confounding variable.