Big data and RCT's in surgical oncology: Impact on improving hepatopancreatobiliary cancer surgical care on the...

Randomized controlled clinical trials (RCTs) are at the heart of "evidence-based" medicine. Conducting well-designed RCTs for surgical procedures is often challenged by inadequate recruitment accrual, blinding, or standardization of the surgical procedure, as well as lack of funding and evolution of the treatment strategy during the many years over which such trials are conducted. In addition, most clinical trials are performed in academic high-volume centers with highly selected patients, which may not necessarily reflect a "real-world" practice setting. Large databases provide easy and inexpensive access to data on a large and diverse patient population at a variety of treatment centers. Furthermore, large database studies provide the opportunity to answer questions that would be impossible or very arduous to answer using RCTs, including questions regarding health policy efficacy, trends in surgical practice, access to health care, the impact of hospital volume, and adherence to practice guidelines, as well as research questions regarding rare disease, infrequent surgical outcomes, and specific subpopulations. Prospective data registries may also allow for quality benchmarking and auditing. There are several high-quality RCTs providing evidence to support current practices in hepatopancreatobiliary (HPB) oncology. Evidence from big data bridges the gap in several instances where RCTs are lacking. In this article, we review the evidence from RCTs and big data in HPB oncology identify the existing lacunae, and discuss the future directions of research in HPB oncology.