Abstract
Several advances in large data set collection and processing have the potential to provide a wave of new insights and improvements in the use of radiation therapy for cancer treatment. The era of electronic health records, genomics, and improving information technology resources creates the opportunity to leverage these developments to create a learning healthcare system that can rapidly deliver informative clinical evidence. By merging concepts from comparative effectiveness research with the tools and analytic approaches of “big data,” it is hoped that this union will accelerate discovery, improve evidence for decision making, and increase the availability of highly relevant, personalized information. This combination offers the potential to provide data and analysis that can be leveraged for ultra-personalized medicine and high-quality, cutting-edge radiation therapy.
Highlights
A classical tenet of evidence-based medicine is that the gold standard evidence to evaluate any intervention is a prospective, phase III randomized controlled trial (RCT) that is appropriately powered, has mature follow-up, and valid statistical analysis
Evidence from relevant RCTs would be available for each potential medical intervention with data applicable to each patient seen in the oncology clinic
RCTs often require a long time to design, conduct, and mature data, and so even well-designed studies can take decades to report meaningful results, at which time, the question asked could no longer be relevant due to other advances or trends in the diagnosis and treatment of cancer. Another significant problem with cancer RCTs is that the results of well-designed trials are commonly not applicable to patients seen in the clinic, given potential differences between patients enrolled in RCTs, who are required to meet strict eligibility criteria, and patients seen in real-world clinics, who may have a complex health history and myriad comorbidities
Summary
A classical tenet of evidence-based medicine is that the gold standard evidence to evaluate any intervention is a prospective, phase III randomized controlled trial (RCT) that is appropriately powered, has mature follow-up, and valid statistical analysis. RCTs often require a long time to design, conduct, and mature data, and so even well-designed studies can take decades to report meaningful results, at which time, the question asked could no longer be relevant due to other advances or trends in the diagnosis and treatment of cancer Another significant problem with cancer RCTs is that the results of well-designed trials are commonly not applicable to patients seen in the clinic, given potential differences between patients enrolled in RCTs, who are required to meet strict eligibility criteria, and patients seen in real-world clinics, who may have a complex health history and myriad comorbidities
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
