CrossCarry: An R package for the analysis of data from a crossover design with GEE

TL;DR

CrossCarry is an R package that simplifies the analysis of crossover design data by extending GEE methods to handle complex correlations, treatment effects, and carry-over effects across various scientific fields.

Contribution

It introduces a comprehensive, open-source R package that models crossover data with flexible correlation structures and both parametric and nonparametric components.

Findings

Provides a unified framework for crossover data analysis

Handles complex correlation and carry-over effects

Facilitates reproducible research across disciplines

Abstract

Crossover designs are widely applied in medicine, agriculture, and other biological sciences, yet their analysis remains challenging due to longitudinal observations within each unit and the presence of carry-over effects. Despite their prevalence, there is no comprehensive R package dedicated to the statistical modeling of crossover data. The CrossCarry package addresses this gap by providing a flexible and open-source framework for analyzing any crossover design with response variables from the exponential family, with or without washout periods. It extends the generalized estimating equations (GEE) methodology by incorporating correlation structures specifically tailored to crossover data, capturing both within- and between-period dependencies. Moreover, CrossCarry integrates a parametric component for treatment effects and a nonparametric spline-based component for time and carry-over effects. This combination allows users to model complex correlation patterns and temporal structures with minimal coding effort. By offering a domain-independent implementation of advanced statistical methodology, CrossCarry facilitates reproducible research and promotes the reuse of robust analytical tools across disciplines. Its potential applications span medical trials, agricultural field experiments, and other areas where crossover designs are essential, thus contributing to broader scientific discovery and cross-domain methodological standardization.