How to be SMART in oncology: A practical framework to assess the contribution of phase using sequential multiple adaptive randomized treatment designs.

Authors

Kristine R Broglio, Elizabeth F Krakow, Erica E M Moodie

Published in

Clinical trials (London, England). Pages 17407745261471462. Aug 31, 2026. Epub Aug 31, 2026.

Abstract

Sequential multiple adaptive randomized treatment designs can generate registrational-quality evidence for the contribution of phase in perioperative oncology by preserving randomized comparisons and intent-to-treat estimation while improving efficiency relative to traditional three-arm trials, and yet they remain underappreciated. We present a sequential multiple adaptive randomized treatment design framework for perioperative regimens with neoadjuvant and adjuvant phases, motivated by perioperative immunotherapy in resectable non-small-cell lung cancer, where the benefit of neoadjuvant therapy is hypothesized to be greater than that of adjuvant therapy.
We define analyses for perioperative-versus-control, neoadjuvant-only-versus-control, and contribution of phase (incremental adjuvant benefit). We specify intent-to-treat analysis sets and describe unbiased estimation.
In a hypothetical trial calibrated to a perioperative setting, a sequential multiple adaptive randomized treatment design increases the number of events for regimen-versus-control comparisons and isolates a randomized comparison for the contribution of phase at the second randomization. Compared with a traditional multi-arm design, simulations demonstrate controlled family-wise type I error (<0.05) and higher power.
Sequential multiple adaptive randomized treatment designs provide a principled, practical approach to within-trial contribution of phase in confirmatory oncology. Sequential multiple adaptive randomized treatment designs offer an efficient approach that addresses regulatory concerns and provides valid and robust inference. Because sequential multiple adaptive randomized treatment designs formalize the iterative nature of clinical decision-making, they are uniquely poised to explicitly answer questions related to overtreatment or undertreatment.

PMID:
42671149
Bibliographic data and abstract were imported from PubMed on 01 Sep 2026.

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