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Robust analysis of stepped wedge trials using composite likelihood models.

Emily C Voldal1, Avi Kenny2,3, Fan Xia4

  • 1Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Statistics in Medicine
|June 5, 2024
PubMed
Summary
This summary is machine-generated.

A new composite likelihood method for stepped wedge trials (SWTs) offers a robust and efficient alternative to traditional mixed models. This approach enhances analysis by effectively using vertical data, improving accuracy for cluster-randomized studies.

Keywords:
cluster randomized trialscomposite likelihoodsmixed effects modelsrobust inferencestepped wedgevertical estimators

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Stepped wedge trials (SWTs) are cluster randomized trials with inherent time-treatment confounding.
  • Traditional mixed models for SWTs are prone to misspecification due to complex correlation structures.
  • Existing non-parametric methods are robust but lack efficiency.

Purpose of the Study:

  • To propose a novel composite likelihood method for analyzing SWTs.
  • To develop a method that is robust to model misspecification while retaining efficiency.
  • To provide a valuable tool for researchers analyzing complex longitudinal cluster data.

Main Methods:

  • Development of a composite likelihood method focusing on vertical (between-cluster) information.
  • Incorporation of horizontal (within-cluster) information for enhanced efficiency.
  • Simulation studies using COVID-19 data and application to the LIRE trial for validation.

Main Results:

  • The proposed vertical composite likelihood model demonstrates increased robustness compared to traditional methods.
  • This method is more efficient than non-parametric approaches relying solely on vertical information.
  • Leveraging baseline data within the composite likelihood model improves performance.

Conclusions:

  • Model-based vertical methods, specifically the proposed composite likelihood approach, offer a promising advancement for SWT analysis.
  • This method provides a robust and efficient alternative, particularly valuable for SWTs with numerous clusters.
  • The findings encourage the adoption of these advanced statistical tools to address concerns about model misspecification in SWTs.