Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).

Abstract

Adaptive interventions (AIs) are increasingly becoming popular in behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for non-inferiority/equivalence testing methods, since AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or non-inferior to those produced by a more costly/burdensome standard of care. Here, we develop data analytic methods and sample size formulas for SMART studies aiming to test the non-inferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online sample size planning resources are provided. A simulated data analysis shows how to test a non-inferiority or equivalence hypothesis with SMART data. For illustration, we use an example from a SMART study aiming to develop an AI for promoting weight loss among overweight/obese adults.

Sequential Multiple Assignment Randomized Trial (SMART) To Identify Optimal Sequences Of Telemedicine Interventions For Improving Initiation of Insulin Therapy: A Simulation Study

“…Such non-inferiority testing methodologies have been applied to conventional RCTs for many years [18]. Very recently, such non-inferiority testing methods [19] along with free web-based software [20], have also been developed in the SMART design context. Availability of such methodology and software tools brings SMARTs to an even playing eld as RCTs, in terms of exibility of hypothesis testing and data analysis.…”

Sequential Multiple Assignment Randomized Trial (SMART) to identify optimal sequences of telemedicine interventions for improving initiation of insulin therapy: A simulation study

“…Such non-inferiority testing methodologies have been applied to conventional RCTs for many years [ 28 ]. Very recently, such non-inferiority testing methods [ 29 ] along with free web-based software [ 30 ], have also been developed in the SMART design context. Availability of such methodology and software tools brings SMARTs to an even playing field as RCTs, in terms of flexibility of hypothesis testing and data analysis.…”

Towards an Eclectic Approach in Autism Spectrum Disorder (Asd)-Smarts (Sequential Multiple Assignment Randomized Trials-Smarts)

“…The SMARTs approach offers the possibility both to collect measurable data based on the established goals and to tailor possible intervention programs adjusting the approach to participants' needs. SMARTs approach is considered a very adequate possibility to conduct an experimental approach in order to build adaptive intervention programs in such a fluid area as SLT in ASD context (Ghosh, Nahum-Shani, Spring, & Chakraborty, 2020;Lavori, & Dawson, 2004;Murphy, 2005;Nahum-Shani, Ertefaie, Lu, Lynch, McKay, Oslin, & Almirall, 2017;Pfammatter et al 2019).…”