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Spatial regression and spillover effects in cluster randomized trials with count outcomes.

Karim Anaya-Izquierdo1, Neal Alexander2

  • 1Department of Mathematical Sciences, University of Bath, Bath, UK.

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Summary

This study introduces a new method for analyzing cluster randomized trials with count data, accounting for indirect and spatial effects. The approach accurately estimates direct and indirect effects, even with model misspecification, using spatial regression models.

Keywords:
cluster randomized trialdepthindirect effectspatial dependencespillover effect

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Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Public Health Research

Background:

  • Cluster randomized trials (CRTs) are essential for evaluating interventions but analyzing count outcomes with spatial and indirect effects presents challenges.
  • Existing methods may struggle to accurately disentangle direct, indirect, and spatial influences on count data within CRTs.
  • Spatial confounding can bias intervention effect estimates in geographically distributed studies.

Purpose of the Study:

  • To develop and present a novel methodology for analyzing count outcome data from CRTs.
  • To incorporate both indirect and spatial effects into the analysis of CRTs.
  • To ensure accurate estimation of direct and indirect effects, robust to model misspecification.

Main Methods:

  • Utilized spatial regression models with Gaussian random effects for overdispersed count outcomes.
  • Employed a novel application of a measure of depth within the intervention arm to model indirect effects.
  • Applied orthogonal regression with a modified intrinsic conditional autoregression model to mitigate spatial confounding.

Main Results:

  • The proposed methodology accurately estimates both direct and indirect effects, demonstrating robustness to model misspecification.
  • Spatial confounding was effectively addressed through orthogonal regression and dimensionally reduced random effects.
  • The model provides marginal interpretations for direct and indirect effects in overdispersed count data.

Conclusions:

  • The developed methodology offers a robust framework for analyzing complex CRTs with count outcomes and spatial dependencies.
  • This approach enhances the precision of intervention effect estimation by accounting for indirect and spatial factors.
  • The methodology is applicable to real-world public health interventions, as demonstrated by its use in a dengue vector control trial.