By now, you have learned how to identify confounding in a study, but this is rarely enough. Confounding can lead to biased estimates which essentially defeats the purpose of research. What is the use of a study if we cannot trust its results? To overcome this problem, we always try to control for confounding. In this lecture, I will describe three methods which you can use to control for confounding at the design stage of a study: randomisation, restriction, and matching. The first and admittedly the best available method to control for confounding is randomisation. When we split our sample into exposed and non-exposed at random, we ensure that the distribution of all factors and characteristics that may influence the outcome is similar between the two groups. With a large enough sample, this neutralizes the impact of any potential confounding factors. The beauty of randomisation is that it controls, not only for known confounders, but also for those that we are not even aware of. Unfortunately, randomisation only applies to trials. As you're well aware, we cannot randomise exposure such as smoking or air pollution due to ethical and practical reasons. Therefore, there are certain questions that cannot be answered by conducting a randomised trial. In such cases, we must rely on other methods to control for confounding. Restriction is such a method. The idea behind restriction is very simple. We restrict the eligibility criteria for subjects to be included in the sample so that we only study subjects within one category of the confounding variable. For instance, if we think that sex may be a confounder, we can decide to restrict our study to women. This solves the problem of confounding in a simple, efficient, and inexpensive way. On the other hand, it might make recruitment of participants more difficult, and in any case, it undermines the generalizability of the study. In the previous example, finding that the drug is effective among women does not necessarily mean that it would be equally effective among men. The third method to control for confounding, which is quite popular for case-control studies, is matching. In matching, we pair one or more controls to each case based on their similarity with regard to selected variables which will consider potential confounders. For instance, we suspect that sex and age maybe confounders in our study. We'll recruit a case who is a woman aged 54 years. If we conduct a match case-control study, we need to find one or more controls that are 50-year old women. This can increase statistical power in our study, but it requires analytical methods that consider the match design. Also, there's a limit to the number of confounders that we can control for with matching. If we try to match on too many variables, recruitment of controls becomes impractical. We're also unable to study the variable we use for matching. Importantly, matching cannot be undone, and matching on a variable that is not a confounder actually harms statistical efficiency. So, a decision to match should be well thought out. I described individual matching, but you should be aware that there is an alternative that provides more flexibility, and is called frequency matching. You should now have an overview of the methods available to control for confounding when designing a study. There are advantages and disadvantages. However, it is not always possible to anticipate and control for confounding at the design stage. Luckily, there are additional methods that can be applied during data analysis.