Pattern-Mixture-Type Estimation and Testing of Neuroblastoma Treatment Regimes

Abstract

Sequentially randomized designs are commonly used in biomedical research, particularly in clinical trials, to assess and compare the effects of different treatment regimes. In such designs, eligible patients are first randomized to one of the initial therapies, and then patients with some intermediate response (e.g., without progressive diseases) are randomized to one of the maintenance therapies. The goal is to evaluate dynamic treatment regimes consisting of an initial therapy, the intermediate response, and a maintenance therapy. In this article, we demonstrate the use of the pattern-mixture model (commonly used for analyzing missing data) for estimating the effects of treatment regimes based on familiar survival analysis techniques such as Nelson-Aalen and parametric models. Moreover, we demonstrate how to use estimates from pattern-mixture models to test for the differences across treatment regimes in a weighted log-rank setting. We investigate the properties of the proposed estimators and test in a Monte Carlo simulation study. Finally, we demonstrate the methods using the long-term survival data from the high-risk neuroblastoma study.