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Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized

Nicholas J Seewald1, Kelley M Kidwell2, Inbal Nahum-Shani3

  • 1Department of Statistics, University of Michigan, Ann Arbor, MI, USA.

Statistical Methods in Medical Research
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This study provides sample size formulas for sequential multiple-assignment randomized trials (SMART) to personalize treatments. These formulas help researchers efficiently design studies for effective dynamic treatment regimens.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Personalized Medicine

Background:

  • Personalizing interventions is crucial for effective healthcare.
  • Dynamic treatment regimens (DTRs) offer tailored, sequential treatment strategies.
  • Sequential Multiple-Assignment Randomized Trials (SMART) are key for developing DTRs.

Purpose of the Study:

  • Derive easy-to-use sample size formulas for two-stage SMART designs.
  • Compare mean end-of-study outcomes for embedded DTRs.
  • Facilitate efficient sample size calculation in DTR research.

Main Methods:

  • Developed sample size formulas for three common two-stage SMART designs.
  • Utilized a regression model incorporating longitudinal outcome data.
  • Formulas integrate standard trial size, longitudinal analysis efficiency, and SMART design factors.

Main Results:

  • Sample size formula for SMART is a product of standard trial size, a deflation factor (longitudinal efficiency), and an inflation factor (SMART design).
  • The SMART design inflation factor depends on first-stage treatment response probability.
  • Provided methods for modeling, estimation, and standard error calculation for DTR effects using longitudinal data.

Conclusions:

  • The derived formulas simplify sample size computation for SMART studies.
  • Longitudinal data analysis enhances statistical efficiency in SMART trials.
  • Methods are applicable to developing personalized DTRs, as demonstrated by the ENGAGE study.