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Optimal allocation strategies in platform trials with continuous endpoints.

Marta Bofill Roig1, Ekkehard Glimm2, Tobias Mielke3

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Statistical Methods in Medical Research
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Summary
This summary is machine-generated.

Determining optimal patient allocation in platform trials is complex due to changing numbers of treatment arms. This study derives new optimal allocation rates for platform trials with shared controls, improving upon traditional methods.

Keywords:
Platform trialoptimal allocationshared controlstime trends

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Drug Development

Background:

  • Platform trials enable simultaneous comparison of multiple interventions against a common control.
  • The number of experimental arms in platform trials can change dynamically over time.
  • Optimal patient allocation in these trials is challenging due to time-varying arm numbers and analysis strategies.

Purpose of the Study:

  • To derive optimal treatment allocation rates for platform trials with shared controls.
  • To address the complexities arising from time-varying arm numbers and different analysis strategies.
  • To minimize the maximum variance of effect estimators under fixed total sample size.

Main Methods:

  • Derivation of optimal allocation rates for platform trials with shared controls.
  • Utilizing stratified estimation and a regression-based testing procedure to adjust for time trends.
  • Considering analyses with concurrent controls only and with both concurrent and non-concurrent controls.
  • Minimizing the maximum variance of effect estimators as the objective function.

Main Results:

  • Optimal allocation rates are dependent on the entry time of arms into the trial.
  • The derived optimal solution generally differs from the square root of N allocation rule used in classical multi-arm trials.
  • Case study illustrates optimal allocation and evaluates power and type 1 error rates compared to standard allocations.

Conclusions:

  • The study provides optimal allocation rates for dynamic platform trials, moving beyond traditional fixed-arm trial methods.
  • Optimal allocation strategies are crucial for efficient platform trial design and reliable results.
  • The findings offer practical guidance for designing and analyzing complex adaptive clinical trials.