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Bayesian sequential designs in studies with multilevel data.

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  • 1Department of Methodology and Statistics, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands. m.moerbeek@uu.nl.

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Summary
This summary is machine-generated.

Bayesian sequential designs offer a flexible alternative for studies with clustered data. This method uses Bayes factors to determine hypothesis support, allowing for adaptive inclusion of more clusters if needed.

Keywords:
Bayes factorBayesian updatingInformative hypothesesInterim analyses

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Area of Science:

  • Social and behavioral sciences
  • Multilevel modeling
  • Statistical study design

Background:

  • Many social and behavioral science studies feature multilevel data structures where subjects are nested within clusters.
  • Accurate a priori estimation of effect size and intraclass correlation coefficient is crucial for determining the necessary number of clusters to achieve desired statistical power.
  • Inaccurate estimates can lead to under- or overpowered studies, necessitating adjustments in study design.

Purpose of the Study:

  • To introduce Bayesian sequential designs as a flexible alternative to traditional group-sequential designs for studies with multilevel data.
  • To explain the methodology of Bayesian sequential designs, focusing on the use of Bayes factors for hypothesis evaluation and decision-making.
  • To investigate the performance of Bayesian sequential designs through a simulation study.

Main Methods:

  • The study introduces Bayesian sequential designs, which compare hypotheses based on data-supported evidence using Bayes factors.
  • If no hypothesis achieves sufficient support, additional clusters are incorporated, and the Bayes factor is recalculated iteratively.
  • A simulation study was conducted to evaluate the impact of varying the minimum and maximum number of clusters per group and the desired level of support in a two-group comparison setting.

Main Results:

  • Bayesian sequential designs provide a flexible approach to managing sample size in studies with multilevel data.
  • The simulation study demonstrated the feasibility and adaptability of this Bayesian approach under different design parameters.
  • The findings suggest that Bayesian sequential designs can effectively address issues of under- or overpowering common in traditional designs.

Conclusions:

  • Bayesian sequential designs are presented as a viable and flexible alternative to group-sequential designs for multilevel studies.
  • The use of Bayes factors allows for adaptive data collection and hypothesis testing, enhancing study efficiency.
  • This approach offers a robust framework for researchers in the social and behavioral sciences dealing with complex data structures.