Bayesian integration G-formula for platform SMART designs allowing for adding new treatments

TL;DR

This paper introduces a Bayesian G-formula method for platform SMART designs, enabling the addition of new treatments during ongoing trials and accounting for non-concurrent comparisons.

Contribution

It proposes a novel platform SMART design integrating platform trials with SMARTs and develops Bayesian estimators for treatment effect estimation.

Findings

BIG estimators perform well in simulations

The method effectively handles non-concurrent treatment comparisons

Application demonstrated on the SNAP trial

Abstract

Dynamic treatment regimes (DTRs) are sequences of decision rules to guide treatment assignments in response to a patient's evolving, time-varying disease status. Sequential multiple assignment randomized trials (SMARTs) are considered the gold standard experimental design for evaluating DTRs. However, SMARTs often require more time to complete compared with a single stage RCT and new candidate treatments may become available or feasible during the trial. Platform trials are an adaptive trial design that allow new treatments to be added to the ongoing study according to a prespecified master protocol. In this paper, we introduce a novel platform SMART that integrates features from both platform trials and SMARTs, allowing new treatments to be added during the trial. Additionally, we propose the Bayesian integration G-formula (BIG) estimators for platform SMARTs to account for non-concurrent treatment comparisons. Extensive simulations are conducted to evaluate the performance of different BIG estimators against benchmark methods. We demonstrate the proposed BIG estimators based on the S. aureus Network Adaptive Platform (SNAP) trial.