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Comparing the MAMS framework with the combination method in multi-arm adaptive trials with binary outcomes.

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This summary is machine-generated.

The extended Multi-Arm Multi-Stage (MAMS) framework now controls the family-wise error rate (FWER). This approach shows favorable comparisons in adaptive trials using the same outcome for all analyses, especially with a new hybrid selection rule.

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MAMSMulti-arm adaptive trialscombination methodfamily-wise error ratelog odds ratioselection rule

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methodology

Background:

  • Multi-arm adaptive trials allow simultaneous assessment of multiple treatments.
  • Data accumulation informs trial decisions like treatment dropping or continuation.
  • The MAMS(R) approach previously lacked guaranteed family-wise error rate (FWER) control, limiting its inclusion in comparisons.

Purpose of the Study:

  • To evaluate the extended MAMS(R) framework for multi-arm two-stage trials with binary outcomes.
  • To compare the extended MAMS(R) with the combination method under different outcome usage scenarios.
  • To introduce a hybrid selection rule for MAMS(R) in comparative treatment selection trials.

Main Methods:

  • Simulation study comparing extended MAMS(R) and combination methods.
  • Parameterization of treatment effects using log odds ratio for binary outcomes.
  • Evaluation of trials using a different outcome for mid-trial analysis versus the same outcome throughout.

Main Results:

  • The extended MAMS(R) framework demonstrates strong FWER control.
  • MAMS(R) favorably compares to the combination method specifically when the same outcome is used throughout the trial.
  • The proposed hybrid selection rule enables MAMS(R) application in comparative treatment selection.

Conclusions:

  • The extended MAMS(R) framework offers an efficient design with guaranteed FWER control.
  • MAMS(R) is particularly advantageous in adaptive trials using a consistent outcome measure.
  • The hybrid selection rule enhances MAMS(R) versatility for comparative treatment selection in adaptive trials.