Response-adaptive randomization (RAR) has been studied extensively in conventional, single-stage clinical trials, where it has been shown to yield ethical and statistical benefits, especially in trials with many treatment arms. However, RAR and its potential benefits are understudied in sequential multiple assignment randomized trials (SMARTs), which are the gold-standard trial design for evaluation of multi-stage treatment regimes. We propose a suite of RAR algorithms for SMARTs based on Thompson Sampling (TS), a widely used RAR method in single-stage trials in which treatment randomization probabilities are aligned with the estimated probability that the treatment is optimal.
View Article and Find Full Text PDFBackground: Epilepsy is a common, chronic pediatric neurological condition predominately treated with anti-seizure medications (ASMs) to control or reduce seizures. Approximately 60 % of youth with epilepsy demonstrate suboptimal adherence to their ASM. This paper describes the methodology, recruitment, design, and baseline participant characteristics of a sequential, multiple assignment, randomized trial (SMART) designed to test the effectiveness of a behavioral health intervention to improve adherence in families of young children with epilepsy.
View Article and Find Full Text PDFSequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody-drug conjugate, datopotamab-deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer.
View Article and Find Full Text PDFAmong the goals of patient-centric care are the advancement of effective personalized treatment, while minimizing toxicity. The phase 2 I-SPY2.2 trial uses a neoadjuvant sequential therapy approach in breast cancer to further these goals, testing promising new agents while optimizing individual outcomes.
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