In this talk, we would like to introduce openstatsware, an official working group of the American Statistical Association (ASA) Biopharmaceutical Section. The working group has a primary objective to engineer R packages that implement important statistical methods, with current focus on mixed models for repeated measures (MMRM) for both frequentist and Bayesian inferences, and health technology assessment (HTA). The secondary objective is to develop and disseminate best practices for engineering high-quality open-source statistical software, for which we have given workshops in different countries/cities and made Youtube video series for education purpose. We would also like to introduce the R package {mmrm} for implementing MMRM, which was developed as a cross-company collaboration via openstatsware. 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 aim to establish {mmrm} as a new standard for fitting MMRM.
Ya Wang
Gilead
Introducing openstatsware and the R package {mmrm}
In this talk, we would like to introduce openstatsware, an official working group of the American Statistical Association (ASA) Biopharmaceutical Section. The working group has a primary objective to engineer R packages that implement important statistical methods, with current focus on mixed models for repeated measures (MMRM) for both frequentist and Bayesian inferences, and health technology assessment (HTA). The secondary objective is to develop and disseminate best practices for engineering high-quality open-source statistical software, for which we have given workshops in different countries/cities and made Youtube video series for education purpose. We would also like to introduce the R package  mmrm  for implementing MMRM, which was developed as a cross-company collaboration via openstatsware. 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 aim to establish  mmrm  as a new standard for fitting MMRM.