MSR53 Bayesian Parametric Mixture Survival Models in Immuno-Oncology Applications: Leveraging Control Arm Observations to Model Heterogeneous Response in the Experimental Arm

Abstract

Heterogeneous response to an intervention is a common feature of survival patterns in randomized clinical trials (RCTs) of immuno-oncology therapies. This effect is usually inadequately represented by standard parametric models. Instead, it is desirable to employ parametric mixture models (PMMs), which represent the cohort as a combination of two latent subpopulations with distinct survival curves. However, classical PMMs are often too complex to parameterize reliably given the limited observations in RCT data.