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Assessment of Bayesian expected power via Bayesian bootstrap.

Fang Liu1

  • 1Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA.

Statistics in Medicine
|June 26, 2018
PubMed
Summary
This summary is machine-generated.

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Bayesian expected power (BEP) offers a more robust probability of success assessment than traditional power by accounting for effect size uncertainty. New model-free bootstrap methods (BBS and double bootstrap) provide efficient and less subjective BEP calculations.

Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trial Design

Background:

  • Traditional power analysis relies on a single effect size, making it sensitive to assumptions.
  • Bayesian expected power (BEP) accounts for effect size uncertainty, offering a less subjective probability of success measure.
  • Existing BEP methods often use parametric models, which can be complex, computationally intensive, and prone to misspecification.

Purpose of the Study:

  • To introduce a model-free approach for calculating Bayesian expected power (BEP) using the Bayesian bootstrap (BBS).
  • To propose a frequentist counterpart, the double bootstrap technique, for BEP assessment.
  • To demonstrate the utility and advantages of these novel bootstrap methods compared to model-based approaches.

Main Methods:

Keywords:
assurancedouble bootstrapfuture trial simulation (FTS)probability of successweighted average power

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  • The Bayesian bootstrap (BBS) technique is employed to simulate future trials from individual-level pilot data.
  • The empirical BEP is calculated from these simulated trials, avoiding assumptions about data distribution.
  • A double bootstrap technique is proposed as a frequentist alternative, sharing similar properties with BBS.

    • Main Results:

    • The proposed BBS approach is model-free, computationally efficient, and handles multivariate data effectively.
    • The double bootstrap technique offers a frequentist alternative with similar benefits for BEP assessment.
    • Both bootstrap methods simplify the combination of information from multiple pilot studies.

    Conclusions:

    • The Bayesian bootstrap (BBS) and double bootstrap techniques provide flexible, efficient, and less subjective methods for calculating Bayesian expected power (BEP).
    • These model-free approaches overcome the analytical and computational challenges of traditional model-based BEP assessments.
    • The methods are particularly valuable when prior information is limited or when dealing with complex data structures.