A note on improved statistical approaches to account for pseudoprogression.

Responses to immuno-oncology agents are often subject to misinterpretation as apparent tumor growth due to immune infiltration leads to the appearance of progressive disease and can result in the discontinuation of effective therapeutic agents. Better statistical strategies to determine experimental outcomes are needed to distinguish between true and pseudoprogression. We applied time-to-event statistical analyses methods that account for study design features and capture the longitudinal and panoramic aspects of pseudoprogression to test superiority of a combination of RRx-001, a novel tumor-associated macrophage polarizing agent in Phase 2, and an anti-PD-L1 antibody in a myeloma preclinical model, comparing to traditional, mean-based mixed effects modeling approaches that did not show statistical significance. Nonparametric p values for the difference of cumulative incidence rates of time to ≥ 50% tumor growth reduction and its associated restricted mean survival times are computed and found to be statistically significant. Kaplan-Meier description of time-to-volume reduction (≥ 50%) coupled with Cox's proportional hazards model follows the data longitudinally and therefore permits an analysis of immune infiltration resolution, making it an improved method for analysis of preclinical experiments with immuno-oncology agents.