Multistate Markov modelling of long-term outcomes in immunotherapy for metastatic renal cell carcinoma (mRCC) using machine-learned dynamic Bayesian networks.

Abstract

e14049 Background: Patient-level heterogeneity in response to treatment remains a major challenge in cancer immunotherapy. Long-term individual-level modelling of survival, tumour response and safety outcomes jointly can help improve efficacy and durable clinical benefits. Machine-learned Bayesian networks provide a solution to the “black box” problem of other machine-learning approaches. Objectives: To develop a dynamic Bayesian network model for multivariate risk prediction, survival modelling and long-term simulations of immunotherapy patients using a simulated trial dataset. Methods: A simulated randomized clinical trial dataset for second line immunotherapy of patients with renal cell carcinoma was used for analysis. We machine-learned a dynamic Bayesian network from censored data with incorporation of prior clinical knowledge. Classification performance, probability calibration, goodness-of-fit metrics and prognostic variables were calculated following TRIPOD guidelines. Results: The machine-learned graphical model encoded expected relationships between variables with minimal prior information. Visual and numerical goodness-of-fit checks for survival extrapolations showed that the model fitted data well and was appropriate. Probability calibration was < 10% from ideal. Classification performance for overall survival was high ( c-statistic ~0.85) soon after treatment initiation and gradually plateaued over time. Prognostic variables were calculated by treatment arm for overall survival and severe adverse events. Conclusions: Dynamic Bayesian network model was useful for transparently representing joint relationships between all variables in the trial dataset, for multivariate risk prediction and long-term extrapolations that can be used in economic evaluations for health technology assessments in oncology.