Optimising adverse event analysis in clinical trials when dichotomising continuous harm outcomes

Introduction: The assessment of harm in randomized controlled trials is vital to enable a risk-benefit assessment on the intervention under evaluation. Many trials undertake regular monitoring of continuous outcomes such as laboratory measurements, for example, blood tests. Typical practice in a trial analysis is to dichotomize this type of data into abnormal/normal categories based on reference values. Frequently, the proportion of participants with abnormal results between treatment arms are then compared using a chi-squared or Fisher’s exact test reporting a p-value. Because dicotomization results in substantial loss of information contained in the outcome distribution, this increases the chance of missing a opportunity to detect signals of harm. Methods: A solution to this problem is to use the outcome distribution in each arm to estimate the between-arm difference in proportions of participants with an abnormal result. This approach has been developed by Sauzet et. al (2016), and it protects against a loss of information and retains statistical power. Results: In this talk, I will introduce the distributional approach and associated Stata community-contributed command distdicho. I will compare the original analysis of blood test results from a small population drug trial in pediatric eczema with the results using the distributional approach and discuss inference from the trial based on these.