Use of Sequential Multiple Assignment Randomized Trials (SMARTs) in oncology: systematic review of published studies

Abstract

Sequential multiple assignments randomized trials (SMARTs) are a type of experimental design where patients may be randomised multiple times according to pre-specified decision rules. The present work investigates the state-of-the-art of SMART designs in oncology, focusing on the discrepancy between the available methodological approaches in the statistical literature and the procedures applied within cancer clinical trials. A systematic review was conducted, searching PubMed, Embase and CENTRAL for protocols or reports of results of SMART designs and registrations of SMART designs in clinical trial registries applied to solid tumour research. After title/abstract and full-text screening, 33 records were included. Fifteen were reports of trials’ results, four were trials’ protocols and fourteen were trials’ registrations. The study design was defined as SMART by only one out of fifteen trial reports. Conversely, 13 of 18 study protocols and trial registrations defined the study design SMART. Furthermore, most of the records considered each stage separately in the analysis, without considering treatment regimens embedded in the trial. SMART designs in oncology are still limited. Study powering and analysis is mainly based on statistical approaches traditionally used in single-stage parallel trial designs. Formal reporting guidelines for SMART designs are needed.

Postprogression therapy and confounding for the estimated treatment effect on overall survival in phase III oncology trials

“…11 Sequentially multiple randomised assignment trials (SMART) randomise patients at the original treatment assignment and again at the time of progression to determine PPT allocation. [43][44][45] This second randomisation to determine PPT allocation at progression removes systematic confounding from PPT selection, thus providing statistical license to compare dynamic treatment regime pairs by virtue of randomisation. Particularly in an era of increasing PPT effectiveness with targeted therapies and immunotherapies, trialists should consider SMART designs to control for systematic PPT confounding.…”

Machine learning applications and challenges in graft-versus-host disease: a scoping review

“…Adaptive treatment strategies (ATS) – also known as dynamic treatment regimens – are a set of decision rules for assigning therapy to patients in which the input is a highly individualized recommendation each time there is a choice of different treatments [87]. Ultimately, ATS aims to customize the course of therapy to tailor management to the patient's medical background and current health status [88]. ATS can be developed using ML methods, such as Q-learning and deep reinforcement learning, to analyze data from trials or registries and propose the most promising treatment options.…”