Interim Analysis in Sequential Multiple Assignment Randomized Trials for Survival Outcomes

Music City Center 

Sequential multiple assignment randomized trials mimic the actual treatment processes experienced by physicians and patients in clinical settings and inform the comparative effectiveness of dynamic treatment regimes. In such trials, patients go through multiple stages of treatment, and the treatment assignment is adapted over time based on individual patient 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 sequential multiple assignment randomized trials for efficacy regarding survival outcomes. We propose a weighted log-rank Chi-square statistic to account for overlapping treatment paths and quantify how the log-rank statistics at two different analysis points are correlated. Efficacy boundaries at multiple interim analyses can then be established using the Pocock, O'Brien Fleming, and Lan Demets boundaries. We run extensive simulations to evaluate and compare the type I error and power of our proposed method with that of an existing statistic. The methods are demonstrated via an analysis of a neuroblastoma study dataset.

Log-rank Statistics

Dynamic Treatment Regimes

Interim Monitoring

Efficacy Boundaries

Inverse Probability Weighting

Trial Efficiency 

Biopharmaceutical Section