useR! 2024

Mixed models for repeated measures (MMRM) analysis has been extensively used to analyze longitudinal datasets. SAS has been the gold standard for this analysis in the past, and so far R packages fall short for one of the following reasons: model convergence issues, unavailability of covariance structures or adjusted degrees of freedom, or numerical results being far from SAS. To fill in this important gap in the open-source statistical software landscape, a cross-company workstream of openstatsware.org has developed the new {mmrm} R package. A critical advantage of {mmrm} over existing implementations is that it is faster and converges more reliably. It also provides a comprehensive set of features: users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjusted degrees of freedom, extract the least square means estimates using the emmeans package, and use tidymodels for easy model fitting. We introduce the modeling framework, the implementation strategy and discuss open source collaboration as a critical ingredient to success.