Estimating treatment effects with treatment switching via semi-competing risks models: An application to a colorectal cancer study

Treatment switching is a frequent occurrence in clinical trials, where, during the course of the trial, patients who fail on the control treatment may change to the experimental treatment. Treatment switching creates statistical challenges for estimating the causal effect of the treatment. Analyzing the data without accounting for switching yields highly biased and inefficient estimates of the treatment effect. In this paper, in order to accurately assess the treatment effect, we propose a novel class of semiparametric semi-competing risks transition survival models to accommodate switch. Theoretical properties of the proposed model are examined and an efficient expectation-maximization algorithm is derived for obtaining the maximum likelihood estimates. Simulation studies are conducted to demonstrate the superiority of the model compared to the intent-to-treat analysis and other methods proposed in the literature. The proposed method is applied to analyze data from the panitumumab study.