Simulating cancer progression events: state-based model transitions with flexible correlation

Oregon Convention Center 

In advanced cancers, patients undergo multiple different lines of therapies, switching treatments when their disease progresses. Using longitudinal data sources, such as those obtained via Electronic Health Records (EHRs), researchers can study effects of common therapy sequences. New models for studying therapy sequence are needed to inform clinical decisions when prospective trial data is absent; however, to evaluate the performance of such models, a method for simulating EHR-like longitudinal data is required. Here, we develop a method for simulating paths through a state-based model. This method allows transition times to depend on treatments and observed covariates and incorporates within-patient correlation. This is important as patients' outcomes across states may be dependent due to difficult-to-quantify factors (e.g., disease aggressiveness, response to prior therapy, evolution of the mutational landscape). We propose to introduce within-patient correlation using a copula. This flexible class of multivariate models allows for researchers to generate outcomes with a range of within-patient correlation structures: Gaussian, T, and Clayton's copula will be considered.

simulation methods

copula

cancer applications

multi-state models

survival

EHR 

ENAR