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Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Zi Wang1, Yu Cheng2, Abdus S Wahed3

  • 1School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.

Biometrics
|July 7, 2026
PubMed
Summary

This study introduces new methods for early stopping in Sequential Multiple Assignment Randomized Trials (SMARTs) to efficiently evaluate dynamic treatment regimes for survival outcomes. The developed approach uses a weighted log-rank statistic for accurate interim monitoring.

Keywords:
dynamic treatment regimesefficacy boundariesinterim monitoringinverse probability weightinglog-rank statisticstrial efficiency

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Sequential Multiple Assignment Randomized Trials (SMARTs) are crucial for evaluating dynamic treatment regimes by mimicking real-world clinical decision-making.
  • Existing methods for interim monitoring in SMARTs may not fully account for complex treatment pathways and their impact on statistical power.
  • Efficient early termination strategies are needed to optimize resource allocation and patient benefit in adaptive clinical trials.

Purpose of the Study:

  • To develop and evaluate statistically valid global interim monitoring (IM) approaches for Sequential Multiple Assignment Randomized Trials (SMARTs) targeting survival outcomes.
  • To enable early termination of SMARTs for efficacy, thereby improving trial efficiency and resource management.
  • To provide a robust statistical framework for analyzing data from adaptive clinical trial designs.

Main Methods:

  • Proposed a novel weighted log-rank Chi-square statistic to address overlapping treatment paths and quantify correlations between log-rank statistics at different analysis points.
  • Established efficacy boundaries for multiple interim analyses using established methods (Pocock, O'Brien Fleming, Lan-DeMets).
  • Conducted extensive simulations to evaluate the operating characteristics (type I error, power) of the proposed IM procedure against an alternative statistic.

Main Results:

  • The proposed weighted log-rank statistic effectively accounts for complex treatment structures in SMARTs.
  • Simulations demonstrated the performance of the interim monitoring procedure, providing insights into type I error and power.
  • The methods were successfully applied to a real-world neuroblastoma dataset, showcasing practical utility.

Conclusions:

  • The developed global interim monitoring approach offers a statistically sound method for early efficacy termination in SMARTs.
  • The weighted log-rank statistic provides a valuable tool for analyzing dynamic treatment regimes in survival studies.
  • This methodology enhances the efficiency and interpretability of adaptive clinical trials for complex treatment strategies.