Detecting treatment effects in clinical trials without a control group

The randomized controlled trial has been the golden standard for clinical testing of treatment efficacy for the last 70 years. To determine a treatment effect, patients are randomly assigned to a treatment group or a control group. In the control group, patients sometimes do not receive a treatment, only serving as the statistical controls to determine the treatment effect. This is done such that the average measurement of both groups can be compared, and the statistical significance of the treatment effect can be evaluated. However, it is considered unethical to assign patients to a group who do not receive treatment, while there is already an existing effective therapy. This is especially the case when the placebo group concerns a vulnerable group like children, psychiatric patients, and patients suffering from cancer. In this research, a statistical method is developed in which the effect of a medical treatment is tested for without a control group. The idea is that groups of patients undergoing effective treatment will show correlated outcomes. The modeling framework considered in this research provides a way to test for this additional correlation in interval-censored survival data. In a simulation study, it is shown that objective Bayesian inference can be efficiently performed on such data, and additional correlation can be tested for.