P14 Interim Analysis in Sequential Multiple Assignment Randomized Trials for Time-to-event Outcomes

Sequential Multiple Assignment Randomized Trials (SMARTs) have been conducted to mimic the actual treatment processes experienced by physicians and patients in clinical settings and inform comparative effectiveness of dynamic treatment regimes (DTRs). In a SMART design, patients are involved in multiple stages of treatment, and the treatment assignment is adapted over time based on the patient's characteristics such as disease status and treatment history. In this work, we develop and evaluate statistically valid interim monitoring approaches to allow for early termination of SMART trials for efficacy regarding time-to-event outcomes. The development is nontrivial. First, in comparing estimated event rates from different DTRs, log-rank statistics need to be carefully weighted to account for overlapping treatment paths. At a given time point, we can then test for the null hypothesis of no difference among all DTRs based on a weighted log-rank Chi-square statistic. With multiple stages, the number of DTRs is much larger than the number of treatments involved in a typical randomized trial, resulting in many parameters to estimate for the covariance matrix of the weighted log-rank statistics. More challengingly, for interim monitoring, we need to quantify how the log-rank statistics at two different time points are correlated, and each component of the covariance matrix depends on a mixture of event processes which can jump at multiple time points due to the nature of multiple assignments. Efficacy boundaries at multiple interim analyses can then be established using the Pocock and the O'Brien Fleming (OBF) boundaries. We run extensive simulations to evaluate and compare type I error and power for our proposed weighted log-rank Chi-square statistic for DTRs under different boundary specifications. The methods are demonstrated in analyzing a neuroblastoma trial.

Clinical Trial Conduct and Analysis Tools (e.g., Monitoring, Operations, Visualization)

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2024