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Power and Sample Size Calculations for Cluster Randomized Hybrid Type 2 Effectiveness-Implementation Studies.

Melody A Owen1, Geoffrey M Curran2, Justin D Smith3

  • 1Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA.

Statistics in Medicine
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

Type 2 hybrid studies require new statistical methods for sample size calculations in cluster-randomized trials. New methods, including extended combined outcomes and single 1-degree of freedom tests, offer the most statistical power for these complex study designs.

Keywords:
cluster‐randomized trialshybrid type 2implementation scienceimplementation‐effectiveness studies

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Area of Science:

  • Biostatistics
  • Implementation Science
  • Clinical Trials

Background:

  • Hybrid type 2 studies equally prioritize intervention effectiveness and implementation outcomes.
  • These studies often employ cluster-randomized designs, posing statistical challenges like multiple testing.
  • Standard statistical methods are insufficient for powering and calculating sample sizes in this context.

Purpose of the Study:

  • To describe available design methodologies for validly powering hybrid type 2 studies.
  • To provide reliable sample size calculation methods for cluster-randomized hybrid type 2 designs with binary outcomes.
  • To extend existing methods to account for clustering in hybrid type 2 studies.

Main Methods:

  • A literature search identified 18 relevant publications on hybrid type 2 study design.
  • Five statistical methods were identified, with two extended to incorporate clustering.
  • Procedures for powering were described and illustrated using a real-world example (CIRCL-Chicago).

Main Results:

  • The conjunctive test demonstrated higher power compared to traditional p-value adjustment methods.
  • Extended combined outcomes and single 1-degree of freedom tests were found to be the most powerful.
  • The study provides practical guidance for sample size calculations in hybrid type 2 cluster-randomized trials.

Conclusions:

  • Novel statistical approaches are necessary for effective hybrid type 2 study design.
  • Extended combined outcomes and single 1-degree of freedom tests offer superior power for binary outcomes in cluster-randomized settings.
  • These methods enhance the reliability of sample size calculations for complex implementation research.