Treatment Effect Estimation With Potential Outcomes for a Single-Arm Trial Compared With Historical Controls: A Case Study of Locally Recurrent Head and Neck Squamous Cell Carcinoma Patients Treated With Adjuvant Nivolumab.

Abstract

Time/resource constraints might preclude a randomized controlled trial. Single-arm oncology trials with historical controls are an alternative. With causal inference, treatment effect estimates can be computed in the absence of randomization. From a single-arm trial of 39 head and neck squamous cell carcinoma patients treated with adjuvant nivolumab, we compare 2-year disease-free survival (DFS) to untreated historical controls. We resort to the potential outcomes framework known as Rubin's causal model (RCM). For time-to-event outcomes, RCM relies upon survival analysis regression with baseline covariates. We contrast the average treatment effect (ATE) estimated by three survival methods: Cox proportional hazards (CPH) versus machine learning alternatives, random survival forests (RSF), and Bayesian Additive Regression Trees (BART). The ATE in favor of nivolumab: CPH 0.202 (0.098-0.306); RSF 0.159 (0.070-0.248); and BART 0.268 (0.126-0.406). The uncertainty is considerable, yet all three methods show nivolumab is superior to control.