Addressing the Challenges to Surgical Randomization

Background: Randomized controlled trials are considered to be the highest level of evidence in clinical research. However, several pitfalls exist to conducting these studies appropriately particularly in surgical research including the challenge of proper randomization.Purpose: Therefore, the purpose of this chapter is to discuss and address the main challenges to randomization in surgical research.Discussion: The most important features of randomization are that it truly allocates treatment randomly and that assignments are tamper proof so that neither intentional nor unintentional factors can influence randomization. As such, ensuring randomization of both known and unknown prognostic factors limits the likelihood of spurious associations between treatment and outcome. In addition, particularly in surgical research, it is critically important to ensure adequate concealment of allocation in order to ensure that knowledge of which treatment patients will receive is concealed as this has the potential to bias reporting by observers. Several randomization techniques exist including simple randomization which is generally best for trials with larger sample sizes, block randomization which is very helpful in smaller trials, and cluster randomization which is most useful when looking to implement new guidelines or forms of care at the population level. Further, stratified randomization may be performed when investigators want to ensure that an important prognostic variable is equally allocated among treatment arms. This technique is extremely important for reducing selection bias.Conclusion: Overall, surgical trials pose a multitude of challenges with respect to randomization, particularly related to smaller sample sizes, the need to adjust for important prognostic factors, and difficulty with ensuring allocation concealment. However, established best practices and various randomization techniques are available to address these challenges and minimize bias as much as possible.