**In a matched pairs design, treatment options are randomly assigned to pairs of similar participants, whereas in a randomized block design, treatment options are randomly assigned to groups of similar participants.** The objective of both is to balance baseline confounding variables by distributing them evenly between the treatment and the control group.

Matched pairs design works in 2 steps:

- Divide participants into pairs by matching each participant with their closest pair regarding some confounding variable(s) like age or gender.
- Within each pair, randomly assign 1 participant to either the treatment or the control group (and the other will be automatically assigned to the other group).

Randomized block design works in 2 steps:

- Divide participants into several subgroups by putting together those who are similar regarding some confounding variable(s) like age or gender.
- Within each subgroup, randomly assign participants to either the treatment or the control group.

Here’s a figure that summarizes the difference between a matched pairs design and a randomized block design that are both trying to equalize the treatment and control groups with regards to gender and smoking status:

When working with a small sample, using simple randomization alone can produce, just by chance, unbalanced groups regarding the patients’ initial characteristics (for a detailed discussion see: Purpose and Limitations of Random Assignment). In these cases, ensuring equivalence between participants by using either a matched pairs design or a randomized block design will increase the statistical power and precision of the study.

## Where randomized block design is better:

Matched pairs design may not be the best option in the following cases:

- If an eligible participant will have to wait a long time to be randomized because a suitable match is hard to find.
- If paired participants may not be similar regarding other important characteristics.
- If the subgroups have an odd number of participants. In this case, each will be left with 1 unpaired participant. Losing some participants this way can be problematic in cases where we are already working with a small sample, and/or very few participants are eligible for the study.

## Where matched pairs design is better:

Matching is especially useful in cases where participants can be paired with themselves.

For instance, in order to study the effect of a new sunscreen, the new product can be applied to the right arm (the treatment group), and the left arm can be used as control.

## Where a completely randomized design is better than both:

Neither matching nor blocking is necessary in studies with large sample sizes, since in these cases, simple randomization alone is enough to balance study groups.

## References

- Friedman LM, Furberg CD, DeMets DL, Reboussin DM, Granger CB.
*Fundament*als of Clinical Trials. 5th edition. Springer; 2015. - Hulley SB, Cummings SR, Browner WS, Grady DG, Newman TB.
*Designing Clinical Research*. 4th edition. LWW; 2013.