Virtual Trials: Causally-validated treatment effects efficiently learned from an observational brain cancer registry
This publication (for which I am one of the authors!) directly addresses, and largely validates the hypothesis that originally motivated our brain tumor virtual trial project. The paper develops a mathematical model that uses patient-reported data from the foundation's online registry, comparing these to the results of seven clinical trials with analogous inclusion criteria. The results closely mirrored four of the comparison trials. One further comparison was well within the margin of error, and another was still within the error margin, but only barely, (This last trial was a strange one, for recurrent high grade glioma patients who never used chemotherapy before and the number of patients was small.)
The bottom line is that a registry like ours is able to efficiently mirror the results of large randomized trials, and, in so doing, involving many fewer patients, and costing much less. Note that the data used in this paper was from an earlier generation of our registry; The current version is regulatory-grade, using both patient reported-data and medical records for verification.