Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Abstract

Sequential multiple assignment randomized trials (SMARTs) 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 undergo multiple stages of treatment, and the treatment assignment is adjusted over time based on individual patient characteristics, such as disease status and treatment history. In this work, we develop and evaluate statistically valid global interim monitoring (IM) approaches to allow for early termination of SMARTs for efficacy targeting 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 conduct extensive simulations to comparatively evaluate the operating characteristics (type I error and power) of our IM procedure based on the proposed statistic and an existing alternative statistic. The methods are demonstrated via an analysis of a neuroblastoma dataset.