Design of clinical trials in interventional oncology: PROMs

‘In theory, there is no difference between theory and practice. In practice there is’. This quote from Yogi Berra, an American baseball player, famous for his unintentionally witty comments, is very applicable to the clinical research setting, intervention oncology being no exception.

The innovative field of interventional oncology holds the promise of better outcomes for patients. For many scientists and clinicians, it is obvious that minimally invasive interventions will cause less damage to healthy tissues and therefore less side effects. For many, it is obvious that ablating tumours under image guidance will increase precision and thereby effectiveness. For many, it is obvious that treatment in an outpatient setting will lead to lower costs. For many, the benefits of image guided interventions are so obvious that it is not necessary, and even unethical, to perform formal (randomized) comparisons to standard treatment.

Nevertheless, history has shown that theoretical benefits of new interventions do not always translate into real benefits for patients. This needs to be shown in good clinical studies.

For this, we need to agree on a definition of beneficial. As scientist and doctors, we tend to focus on tumour response, RECIST, or biochemical control, but these outcomes are not very relevant for patients. From a patient perspective, a treatment is beneficial when it allows patients to live longer, experience fewer side effects, return to work sooner, and/or enjoy their grandchildren. From a societal perspective, a new treatment is beneficial when the benefits outweigh the costs.

In my talk, I will focus on how we can demonstrate (cost)effectiveness of complex interventions. I will outline that it is crucial to measure patient reported outcomes (e.g. quality of life, physical, social, and cognitive functioning, symptoms, workability, etc.) and outline how to measure and analyze them. I will introduce alternative trial designs and discuss some basic principles of health technology assessment (cost-effectiveness analyses). Finally, I will demonstrate how we can make optimal use of routine care and routine data sources to generate robust (randomized) evidence of effectiveness of new interventions, in an efficient and relatively low-cost fashion.

With this talk, I hope to provide the audience with the tools to embark on, or participate in good clinical research. Generating good clinical evidence will lead to undelayed uptake and implementation of innovative treatments that really work, while preventing implementation of interventions that are ineffective or even harmful. Generating good clinical evidence is therefore the best way to improve outcomes for patients.