Introducing `openstatsware` and the R Package `{mmrm}`

R/Pharma 2023 Conference

Ya Wang on behalf of the working group

2023-10-24

Introducing the Working Group

Primary
- Engineer R packages that implement important statistical methods
  - to fill in gaps in the open-source statistical software landscape
  - focusing on what is needed for biopharmaceutical applications
Secondary
- Develop and disseminate best practices for engineering high-quality open-source statistical software
  - By actively doing the statistical engineering work together, we align on best practices and can communicate these to others

Mixed Models for Repeated Measures (MMRM) 🌎
- Develop mmrm R package for frequentist inference in MMRM (CRAN)
Bayesian MMRM 🌎
- Develop brms.mmrm R package for Bayesian inference in MMRM (CRAN)
Health Technology Assessment 🌎
- Develop open-source R tools to support HTA dossier submission across various countries
- Particularly topics with unmet needs in R implementation and/or related to EUnetHTA

Workshop “Good Software Engineering Practice for R Packages” on world tour
- to teach hands-on skills and tools to engineer reliable R packages
- 5 events so far at Basel, Shanghai, San José, Rockville, and Montreal
Youtube video series “Statistical Software Engineering 101” 🌎
- to introduce tips and tricks for good statistical software engineering practices
- 2 videos so far

MMRM is a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials
No great R package
- Initially thought that the MMRM problem was solved by using lme4 with lmerTest, learned that this approach failed on large data sets (slow, did not converge)
- nlme does not give Satterthwaite adjusted degrees of freedom, has convergence issues, and with emmeans it is only approximate
- Next we tried to extend glmmTMB to calculate Satterthwaite adjusted degrees of freedom, but it did not work

We only want to fit a fixed effects model with a structured covariance matrix for each subject
The idea is then to use the Template Model Builder (TMB) directly - as it is also underlying glmmTMB - but code the exact model we want
We do this by implementing the log-likelihood in C++ using the TMB provided libraries

Fast C++ framework for defining objective functions (Rcpp would have been alternative interface)
Automatic differentiation of the log-likelihood as a function of the variance parameters
We get the gradient and Hessian exactly and without additional coding
This can be used from the R side with the TMB interface and plugged into optimizers

Ongoing maintenance and support from the pharmaceutical industry
- 5 companies being involved in the development, on track to become standard package
Development using best practices as show case for high quality package
- Thorough unit and integration tests (also comparing with SAS results) to ensure accurate results

Linear model for dependent observations within independent subjects
Covariance structures for the dependent observations:
- Unstructured, Toeplitz, AR1, compound symmetry, ante-dependence, spatial exponential
- Allows group specific covariance estimates and weights
REML or ML estimation, using multiple optimizers if needed
emmeans interface for least square means
tidymodels for easy model fitting
Satterthwaite and Kenward-Roger adjustments
Robust sandwich estimator for covariance

We have run comparison analyses with other R packages, namely nlme, glmmTMB and lme4
Also compared with SAS PROC GLIMMIX
Highlights
- mmrm has faster convergence time
- mmrm provides closest results to PROC GLIMMIX
Detailed results at the online comparison vignette