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Partially nested designs with treatment-induced clustering are common in behavioral research. This study introduces an R package, PND.heter.cluster, to estimate cluster-specific treatment effects, enhancing analytical flexibility.

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Area of Science:

  • Psychology
  • Behavioral Science
  • Statistical Methodology

Background:

  • Partially nested designs are prevalent in psychological and behavioral research.
  • Clustering in treatment arms, often due to treatment delivery, is common.
  • Estimating cluster-specific treatment effects in nonrandomized, treatment-induced clustering is challenging.

Purpose of the Study:

  • To introduce an accessible R package for estimating cluster-specific treatment effects in partially nested designs.
  • To address limitations in existing methods for treatment-induced clustering.
  • To provide a flexible tool using machine learning for enhanced estimation.

Main Methods:

  • Development of the R package PND.heter.cluster.
  • Implementation of methods for 2/1 partially nested designs with treatment-induced clustering.
  • Support for machine learning techniques to relax modeling assumptions.

Main Results:

  • The PND.heter.cluster package provides a method for estimating cluster-specific treatment effects.
  • Machine learning integration enhances estimation flexibility in complex designs.
  • A tutorial demonstrates the package's application with real-world data.

Conclusions:

  • The PND.heter.cluster package offers a novel solution for analyzing partially nested data with treatment-induced clustering.
  • The package enhances the ability to explore treatment effect heterogeneity across clusters.
  • This tool facilitates more nuanced understanding in psychological and behavioral research.