Cancer Models and Real-World Data: Better Together
2015
Decision-analytic models synthesize available data on disease burden and intervention effectiveness to project estimates of the long-term consequences of care. While models have been influential in informing US cancer screening guidelines under ideal conditions, incorporating detailed data on real-world screening practice has been limited given the complexity of screening processes and behaviors throughout diverse health delivery systems in the United States.
The authors describe the synergies that exist between decision-analytic models and health care utilization data that are increasingly accessible through research networks that assemble data from the growing number of electronic medical record systems. They present opportunities to enrich cancer screening models by grounding analyses in real-world data with the goals of projecting the harms and benefits of current screening practices, evaluating the value of existing and new technologies, and identifying the weakest links in the cancer screening process where efforts for improvement may be most productively focused.
The example of the National Cancer Institute–funded consortium Population- based Research Optimizing Screening through Personalized Regimens (PROSPR) is provided as an example. This is a collaboration to harmonize and analyze screening process and outcomes data on breast, colorectal, and cervical cancers across seven research centers.
Cancer Models and Real-World Data: Better Together
Source:
Kim J, Tosteson AN, Zauber AG et al. Cancer Models and Real-World Data: Better Together. Journal of the National Cancer Institute 2015; 108 (2). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4907359