In a session at their 21st Annual Meeting in Hollywood, FL, National Comprehensive Cancer Network (NCCN) CEO Robert W Carlson, MD, discussed how NCCN Evidence Blocks stack up against other tools currently available for determining the value of cancer treatments.
The NCCN Evidence Blocks are a fairly recent endeavor by the organization to improve physician decision-making and encourage conversations between patients and providers. The blocks appear as a 5-by-5 matrix, rating the safety, efficacy, quality of evidence, consistency of evidence, and affordability of given treatments on a 1 (worst) to 5 (best) scale to determine an overall “value” associated with treatments.
The Journal of Clinical Pathways previously spoke with Dr Carlson about the NCCN Evidence Blocks in an interview published in its December issue.
In comparison with two of other prominent tools for assessing value, Dr Carlson said that the Evidence Blocks allow for greater flexibility in comparing different drugs then the Value Framework developed by the American Society of Clinical Oncology or the DrugAbacus created by doctors at Memorial Sloan Kettering Cancer Center (New York, NY). Dr Carlson added that the fact that the DrugAbacus tool only accounts for the price of treatments makes the system mostly beneficial to payers and manufacturers. In contrast, Evidence Blocks account for the full value of a treatment, making it beneficial to patients and physicians as well.
Dr Carlson also addressed the recent tool developed by the Institute for Clinical and Economic Review, which he called an interesting model that provides a “fairly rigorous” evaluation of different treatment options, but that it also seems limited by a bias associated with population size, making a drug seem more affordable if only a small number of patients need it.
In the question and answer portion of the session, Dr Carlson was asked how the Evidence Blocks account for subtle distinctions in drug performance. He responded by citing a long-standing issue within the industry: the variable nature of clinical data. For example, a response rate of 80% reported in one study might be the same as a 60% response rate reported by another study after taking into account differences in follow-up duration, statistical variation, or other clinical or research factors.
“We’re trying to get the metrics in the right ballpark, in the right sort of big bucket, rather than trying to split hairs,” Dr Carlson explained.
