Controlled Variable Selection in Generalized Odds Rate Mixture Cure Models for High-dimensional Data

Music City Center 

When modeling a time-to-event outcome, a cured fraction exists when a subgroup of patients is immune to the event of interest. In such cases, rather than fitting a traditional survival model, a mixture cure model (MCM) is used. Additionally, when identifying genomic features associated with a time-to-event outcome, variable selection techniques for high-dimensional settings are needed. However, few variable selection methods exist for fitting MCMs to high-dimensional data. We developed a high-dimensional penalized generalized odds rate (GOR) MCM, which allows for identification of prognostic factors associated with both cure status and/or survival of uncured patients. We implemented the generalized monotone incremental forward stagewise algorithm to estimate the model complemented by the Model-X knockoffs framework to select important variables while controlling the false discovery rate. Through extensive simulations, we demonstrated empirical properties of our penalized GOR-MCM in comparison to alternative methods. In our acute myeloid leukemia application, we further showed controlled variable selection performance of our method in a real-world application setting.

survival analysis

cure fraction

variable selection

penalized

forward stagewise

false discovery rate 

Biometrics Section