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Related Experiment Video

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Simulation and minimization: technical advances for factorial experiments designed to optimize clinical

Jocelyn Kuhn1, Radley Christopher Sheldrick2, Sarabeth Broder-Fingert3,4

  • 1Boston Medical Center, 72 E. Concord St, Boston, MA, USA. jocelyn.kuhn@bmc.org.

BMC Medical Research Methodology
|December 18, 2019
PubMed
Summary
This summary is machine-generated.

Minimization is the best allocation procedure for complex clinical trials, balancing sample sizes and covariates effectively. Computer simulations informed this choice, guiding researchers in selecting optimal randomization methods for multi-arm studies.

Keywords:
Clinical intervention studiesClinical trialsFactorial designMinimizationMulti-phase optimization strategyRandomizationSubject allocation

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Health Services Research

Background:

  • Multiphase Optimization Strategy (MOST) aims to enhance clinical intervention impact.
  • Factorial experiments are key in MOST for identifying effective, efficient, and scalable intervention components.
  • Careful participant assignment in factorial experiments is crucial for balanced sample sizes, covariate equivalence, and unpredictability.

Purpose of the Study:

  • To empirically compare five participant allocation procedures in a 2x2x2x2 factorial design MOST trial.
  • To evaluate simple randomization, stratified randomization with permuted blocks, maximum tolerated imbalance (MTI), minimal sufficient balance (MSB), and minimization.
  • To assess methods based on sample size balance, covariate equivalence, and unpredictability using computer simulations.

Main Methods:

  • Computer simulations were conducted using bootstrap samples of 304 participants.
  • 250 simulations were performed to compare five allocation procedures.
  • Procedures were evaluated across 16 study cells for balance, covariate equivalence, and unpredictability.

Main Results:

  • Simple randomization offered high unpredictability but poor balance and covariate equivalence.
  • Stratified randomization balanced stratified variables but not others.
  • Minimization achieved the best balance and covariate equivalence but had 34% deterministic allocations; MTI and MSB showed limitations in balance or covariate equivalence.

Conclusions:

  • Minimization with a random element was chosen for the planned study due to its superior performance in balancing sample sizes and covariates.
  • Computer simulation is a valuable tool for selecting appropriate randomization procedures in complex experimental designs.
  • Researchers conducting multi-arm and factorial studies should consider advanced randomization techniques like minimization with a random element.