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Creating a Statistical Analysis Plan to Continually Evaluate Intervention Adaptations that Arise in Real-World

Teresa Bufford1, Hilary Aralis2, Sheryl Kataoka3

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Summary
This summary is machine-generated.

This study presents a statistical analysis plan to evaluate health intervention adaptations in real-world settings. It combines platform trial methods with real-world data analysis to ensure accurate findings as interventions evolve.

Keywords:
Covariate balancingIntervention adaptationOngoing analysisReal world evidence

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Area of Science:

  • Public Health
  • Biostatistics
  • Health Services Research

Background:

  • Evidence-based health interventions often require adaptation in real-world settings due to practical constraints.
  • Assessing the comparative effectiveness of these adaptations via traditional randomized trials is often infeasible.
  • Observational data, however, can be leveraged to identify beneficial adaptations using statistical adjustment methods.

Purpose of the Study:

  • To propose a statistical analysis plan for evaluating intervention adaptations during ongoing implementation.
  • To integrate methods from platform clinical trials and real-world data analysis.
  • To ensure low statistical error rates when making multiple comparisons over time.

Main Methods:

  • Developing a statistical analysis plan combining platform trial and real-world data methods.
  • Utilizing simulations based on historical data to determine optimal analysis frequency.
  • Applying the plan to a large-scale school-based resilience and skill-building intervention with adaptations.

Main Results:

  • The proposed plan enables the evaluation of intervention adaptations using observational data.
  • Simulations can guide the frequency of statistical analyses to maintain accuracy.
  • The approach is illustrated with a real-world example of a school-based preventive intervention.

Conclusions:

  • A robust statistical analysis plan can effectively evaluate adaptations to health interventions in real-world implementations.
  • This methodology supports continuous improvement and optimization of interventions as they scale.
  • The approach has the potential to enhance population-level health outcomes through data-driven adaptation strategies.