<i>Cureit</i>: An End-to-End Pipeline for Implementing Mixture Cure Models With an Application to Liposarcoma Data.

Abstract

Cure models are a useful alternative to Cox proportional hazards models in oncology studies when there is a subpopulation of patients who will not experience the event of interest. Although software is available to fit cure models, there are limited tools to evaluate, report, and visualize model results. This article introduces the <i>cureit</i> R package, an end-to-end pipeline for building mixture cure models, and demonstrates its use in a data set of patients with primary extremity and truncal liposarcoma. To assess associations between liposarcoma histologic subtypes and disease-specific death (DSD) in patients treated at Memorial Sloan Kettering Cancer Center between July 1982 and September 2017, mixture cure models were fit and evaluated using the <i>cureit</i> package. Liposarcoma histologic subtypes were defined as well-differentiated, dedifferentiated, myxoid, round cell, and pleomorphic. All other analyzed liposarcoma histologic subtypes were significantly associated with higher DSD in cure models compared with well-differentiated. In multivariable models, myxoid (odds ratio [OR], 6.25 [95% CI, 1.32 to 29.6]) and round cell (OR, 16.2 [95% CI, 2.80 to 93.2]) liposarcoma had higher incidences of DSD compared with well-differentiated patients. By contrast, dedifferentiated liposarcoma was associated with the latency of DSD (hazard ratio, 10.6 [95% CI, 1.48 to 75.9]). Pleomorphic liposarcomas had significantly higher risk in both incidence and the latency of DSD (<i>P</i> < .0001). Brier scores indicated comparable predictive accuracy between cure and Cox models. We developed the <i>cureit</i> pipeline to fit and evaluate mixture cure models and demonstrated its clinical utility in the liposarcoma disease setting, shedding insights on the subtype-specific associations with incidence and/or latency.