Implications of non-compliance in the experimental arm on treatment effect estimation in randomised controlled trials: a methodological assessment of four summary measures

Non-compliance in randomised controlled trials (RCTs) may distort the estimate of treatment effect in intention-to-treat (ITT) and per-protocol (PP) analyses. For time-to-event outcomes, Inverse Probability of Censoring Weighting (IPCW) method and Rank Preserving Structural Failure Time model (RPSFTM) have been proposed to address treatment switch across two arms, with treatment effects summarised in terms of hazard ratio (HR) or restricted mean survival time (RMST). In this paper, we adapt the IPCW and RPSFTM methods to consider one-sided non-compliance affecting the experimental arm. For IPCW, non-compliers in the experimental arm were censored at the time of non-compliance. For RPSFTM, the causal model was modified to reconstruct the counterfactual survival time for non-compliers in the experimental arm. We quantify the treatment effects using HR, difference (dRMST) or ratio (rRMST) of RMST and milestone survival difference (MSD). We compare the aforementioned methods via rigorous simulation and a real-world data analysis based on a randomised trial of nasopharyngeal cancer. The simulation results demonstrated that RPSFTM-related models produced smaller bias, whereas IPCW generally performed well under high non-compliance for all summary measures. However, the RMST-based estimates had lower power than the HR estimate under idealised proportional hazard setting. When illustrated using real-world data, RPSFTM-related models yielded larger estimate of treatment effect for summary measures of dRMST and rRMST, whereas IPCW produced larger estimates when quantified in terms of HR and MSD. For handling treatment non-compliance in RCTs, it is important to choose the summary measure and model carefully on a case-by-case basis.