A new clinical treatment (i.e. **intervention**) is often studied by comparing** **it to a **placebo** or an **existing intervention**. Two or more patient groups with different interventions are created and the **outcomes** are studied. When you compare these outcomes, it is important to make sure you avoid **confounding**. This will increase the likelihood of capturing the true effect the intervention has on the outcome (i.e. the **treatment effect**).

As said before, a few methods to minimize confounding are **restriction, randomization, or matching. **These methods all aim to reduce the baseline differences between intervention groups. When patient characteristics are balanced between groups, they are less likely to **confound** the treatment effect.

**Can I use p-values to assess balance in baseline between groups?**

**P-values** can tell you whether differences between groups are likely caused by chance (i.e. random variation) or not. When a **p-value** is less than 0.05 (the current cut-off), the risk of mistakenly concluding that a difference is caused by chance, is <5%.* ***P-values** are an indispensable part of hypothesis testing, however, they are **overused in clinical research**. *For more details check out this Wikipedia page on p-values.*

One example of **inappropriate use of p-values** is to assess balance in baseline characteristics between intervention groups after an attempt to avoid confounding. Here are two examples why: **(1) After randomization, the chance that observed differences are caused by random variation is 100% (p=1.0). (2) After matching, the p-value is uninformative because there is no immediate relationship between group differences and the p-value.** Therefore, the **p-value** is inadequate to assess and optimize balance at baseline.

**Solution?**

The current best practice for baseline assessment is to use the **standardized mean difference**** (SMD)**. This ratio is calculated by dividing the difference in means between groups by the standard deviation of the variable among all study participants. An SMD of 0 indicates **perfect balance**, whereas an SMD of 1 indicates **infinite imbalance**. A typical rule of thumb for adequate balance is an SMD <0.1 (or 10%). *For more details check out this Cochrane page on the SMD.*

**Using SMDs instead of p-values for baseline comparison will improve the quality of your study.** *However, keep in mind that some (late adapting) journals still require the use of p-values for baseline tables. *