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Bayesian design and analysis of external pilot trials for complex interventions.

Duncan T Wilson1, James M S Wason2,3, Julia Brown1

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|March 18, 2021
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This study introduces a Bayesian approach for external pilot trials, improving decisions on whether to proceed to confirmatory trials. It helps manage multiple endpoints and complex models for better intervention design.

Keywords:
Bayesiancomplex interventionsexternal pilotpilot trialssample size

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Area of Science:

  • Statistics
  • Health Services Research

Background:

  • External pilot trials are crucial for planning confirmatory trials of complex interventions.
  • Progression decisions often rely on pre-specified criteria, but their statistical properties are seldom assessed.
  • Methodological challenges include multiple endpoints, complex models, small sample sizes, and parameter uncertainty.

Purpose of the Study:

  • To present a Bayesian approach for designing and analyzing external pilot trials.
  • To enable informed progression decisions by minimizing expected loss, considering multiple parameters and trade-offs.
  • To assess the operating characteristics of pilot trial designs.

Main Methods:

  • A Bayesian framework for decision-making in external pilot trials.
  • Utilizing a loss function defined over the parameter space to incorporate preferences.
  • Employing a piecewise constant parametrization of the loss function for feasibility.
  • Implementing a nested Monte Carlo algorithm for estimating operating characteristics.

Main Results:

  • The proposed Bayesian approach allows for a principled way to make progression decisions.
  • It facilitates the articulation and incorporation of preferences and trade-offs between multiple trial parameters.
  • The method was applied to redesign an external pilot trial for a complex intervention aimed at increasing physical activity in care home residents.

Conclusions:

  • The Bayesian approach offers a robust method for the design and analysis of external pilot trials.
  • It addresses key methodological challenges, leading to more reliable progression decisions.
  • This framework can enhance the planning and execution of future complex intervention trials.