Interim monitoring in sequential multiple assignment randomized trials

Abstract

A sequential multiple assignment randomized trial (SMART) facilitates the comparison of multiple adaptive treatment strategies (ATSs) simultaneously. Previous studies have established a framework to test the homogeneity of multiple ATSs by a global Wald test through inverse probability weighting. SMARTs are generally lengthier than classical clinical trials due to the sequential nature of treatment randomization in multiple stages. Thus, it would be beneficial to add interim analyses allowing for an early stop if overwhelming efficacy is observed. We introduce group sequential methods to SMARTs to facilitate interim monitoring based on the multivariate chi-square distribution. Simulation studies demonstrate that the proposed interim monitoring in SMART (IM-SMART) maintains the desired type I error and power with reduced expected sample size compared to the classical SMART. Finally, we illustrate our method by reanalyzing a SMART assessing the effects of cognitive behavioral and physical therapies in patients with knee osteoarthritis and comorbid subsyndromal depressive symptoms.

Interim Monitoring of Sequential Multiple Assignment Randomized Trials Using Partial Information

“…where 𝐿 𝑘 (𝐱 𝑘 ) is an arbitrary function of 𝐱 𝑘 and we define ∏ 0 𝑣=1 𝜋 𝑣 (𝐴 𝑣,𝑖 , 𝐇 𝑣,𝑖 ; θ𝑣,𝑁 ) = 1. Setting 𝐿 𝑘 (𝐱 𝑘 ) ≡ 0 yields the IPWE that forms the basis for the approach of Wu et al (2021); the IPWE uses only the observed outcomes and no covariate information. It can be an inefficient estimator for 𝑉(𝐝) when there are covariates that are correlated with the outcome.…”

“…This formulation can be adapted to any set of hypotheses involving functions of the values of regimes of interest. For example, testing the homogeneity hypothesis (1) would involve calculation of chi-square test statistics based on the distributions of (𝑡 𝑠 ), 𝑠 = 1, 2, analogous to Wu et al (2021), which would be compared to corresponding stopping boundaries.…”

“…We report on extensive simulations to evaluate the performance of the IAIPWE. In our simulation settings, IPWE corresponds to the proposed method of Wu et al (2021).…”

“…However, analogous methodology for SMARTs is limited. Wu et al (2021) propose an interim test for a difference in mean outcome among embedded regimes in two-stage SMARTs. However, their approach is based on the inverse probability weighted estimator (IPWE), which does not incorporate baseline and accruing patient information that could be used to enhance efficiency (Zhang et al, 2013).…”

“…Using the asymptotic distribution of this estimator, we derive associated Pocock and O'Brien-Fleming testing procedures for early stopping. In simulation experiments, the estimator controls type I error and achieves nominal power while reducing expected sample size relative to the method of Wu et al (2021). We present an illustrative application of the proposed estimator based on a recent SMART evaluating behavioral pain interventions for breast cancer patients.…”