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Design and Analysis Considerations for Comparing Dynamic Treatment Regimens with Binary Outcomes from Sequential

Kelley M Kidwell1, Nicholas J Seewald2, Qui Tran1

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, USA.

Journal of Applied Statistics
|December 18, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a simple method for comparing dynamic treatment regimens (DTRs) within sequential multiple assignment randomized trials (SMARTs). The approach includes sample size calculations to help design effective DTRs for personalized medicine.

Keywords:
Adaptive interventionsDynamic treatment regimesInverse-probability-of-treatment weightingSequential multiple assignment randomized trialsample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Personalized Medicine

Background:

  • Interventions in behavioral, educational, and medical fields are increasingly personalized over time.
  • Dynamic Treatment Regimens (DTRs) mimic real-world practice by adapting treatments based on individual responses.
  • Sequential Multiple Assignment Randomized Trials (SMARTs) are a robust design for developing effective DTRs.

Purpose of the Study:

  • To review a 'weighted and replicated' estimation technique for comparing DTRs within SMART designs.
  • To present a sample size calculation method for designing SMARTs with binary outcomes.
  • To provide analytic models and calculations for common two-stage SMART designs.

Main Methods:

  • Utilized logistic regression for a binary, end-of-study outcome.
  • Employed a 'weighted and replicated' estimation technique for comparing embedded DTRs.
  • Developed a sample size calculation based on a Wald test for comparing two DTRs.

Main Results:

  • Simulations confirmed that the sample size calculation achieves expected empirical power.
  • The 'weighted and replicated' method provides a simple approach for DTR comparison in SMARTs.
  • Demonstrated the application of the methods with an example from autism research.

Conclusions:

  • The reviewed estimation technique and sample size calculation facilitate the design and analysis of SMARTs for developing DTRs.
  • This methodology supports the creation of adaptive and personalized treatment strategies.
  • The findings are applicable to various fields requiring individualized intervention approaches.