A progression model for repeated measures (PMRM) is a continuous-time nonlinear mixed-effects model for longitudinal clinical trials in progressive diseases. Unlike mixed models for repeated measures (MMRMs), which estimate treatment effects as linear combinations of additive effects on the outcome scale, PMRMs characterize treatment effects in terms of the underlying disease trajectory. This framing yields clinically interpretable quantities such as average time saved and percent reduction in decline due to treatment. The pmrm package implements the frequentist PMRM framework of Raket (2022) using automatic differentiation via RTMB (Kristensen et al. 2016).
There are multiple ways to install pmrm.
| Type | Source | Command |
|---|---|---|
| Release | CRAN | install.packages("pmrm") |
| Release | GitHub | pak::pkg_install("openpharma/pmrm@*release") |
| Development | GitHub | pak::pkg_install("openpharma/pmrm") |
To cite pmrm in publications, please use:
Landau WM, Raket LL, Kristensen K (2026). "Progression models for
repeated measures: Estimating novel treatment effects in progressive
diseases." R package version 0.0.1,
<https://openpharma.github.io/pmrm>.
A BibTeX entry for LaTeX users is
@Misc{,
author = {William Michael Landau and Lars Lau Raket and Kasper Kristensen},
title = {Progression models for repeated measures: Estimating novel treatment effects in progressive diseases},
year = {2026},
note = {R package version 0.0.1},
url = {https://openpharma.github.io/pmrm},
}
Please also cite the underlying methods paper:
Raket, Lars Lau (2022). Progression Models for Repeated Measures:
Estimating Novel Treatment Effects in Progressive Diseases.
Statistics in Medicine, 41(28), 5537–57,
https://doi.org/10.1002/sim.9581.
A BibTeX entry for LaTeX users is
@Article{,
author = {Lars Lau Raket},
title = {Progression models for repeated measures: Estimating novel treatment effects in progressive diseases},
journal = {Statistics in {M}edicine},
year = {2022},
volume = {41},
number = {28},
pages = {5537-5557},
doi = {10.1002/sim.9581},
url = {https://doi.org/10.1002/sim.9581},
}
Kristensen, Kasper, Anders Nielsen, Casper W. Berg, Hans Skaug, and Bradley M. Bell. 2016. “TMB: Automatic Differentiation and Laplace Approximation.” Journal of Statistical Software 70 (5): 1–21. https://doi.org/10.18637/jss.v070.i05.
Raket, Lars Lau. 2022. “Progression Models for Repeated Measures: Estimating Novel Treatment Effects in Progressive Diseases.” Statistics in Medicine 41 (28): 5537–57. https://doi.org/10.1002/sim.9581.