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Penalized GEE for Complex Carry-Over in Repeated-Measures Crossover Designs.

Nelson Alirio Cruz1,2,3, Oscar Orlando Melo4, Kalliopi Mylona5

  • 1Departament de Matemàtiques i Informàtica, Universitat de les Illes Balears, Palma de Mallorca, Spain.

Statistics in Medicine
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to accurately estimate complex carry-over effects in crossover designs. The penalized semiparametric Generalized Estimating Equations (GEE) approach improves analysis by accounting for order-dependent treatment interactions.

Keywords:
carry‐over effectcorrelated datageneralized estimating equationskronecker correlationparameter estimability

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Area of Science:

  • Biostatistics
  • Clinical Research Methodology
  • Statistical Modeling

Background:

  • Crossover designs are widely used in clinical and behavioral research.
  • Existing statistical models often make oversimplified assumptions about carry-over effects, ignoring their complexity.
  • Complex carry-over effects, varying by treatment order and interaction, have lacked formal statistical estimation methods.

Purpose of the Study:

  • To introduce a novel statistical methodology for estimating complex first-order carry-over effects in repeated-measures crossover designs.
  • To establish identifiability conditions for complex carry-over effects.
  • To provide theoretical guarantees for the proposed estimation approach.

Main Methods:

  • Development of a penalized semiparametric Generalized Estimating Equations (GEE) approach.
  • Derivation of identifiability conditions for complex carry-over effects.
  • Extension of the sandwich variance formula to provide asymptotic normality guarantees.
  • Shrinking negligible carry-over effects towards zero for practical identification.

Main Results:

  • The proposed penalized GEE method accurately estimates complex carry-over effects.
  • Theoretical guarantees demonstrate asymptotic normality for functional components.
  • The method effectively identifies and quantifies complex, order-dependent carry-over effects.
  • Simulation studies and real-data applications show improved estimation accuracy over simpler models.

Conclusions:

  • This work presents the first rigorous and generalizable approach for modeling complex carry-over effects in repeated-measures crossover designs.
  • The penalized semiparametric GEE method offers improved accuracy and practical identification of complex carry-over effects.
  • This methodology enhances the statistical analysis of crossover trials where treatment order influences outcomes.