Package: pmrm 0.0.4.9000

William Michael Landau

pmrm: Progression Models for Repeated Measures

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. This package implements frequentist PMRMs by Raket (2022) <doi:10.1002/sim.9581> using 'RTMB' by Kristensen (2016) <doi:10.18637/jss.v070.i05>.

Authors:William Michael Landau [aut, cre], Lars Lau Raket [aut], Kasper Kristensen [aut], Eli Lilly and Company [cph, fnd]

pmrm_0.0.4.9000.tar.gz
pmrm_0.0.4.9000.zip(r-4.7)pmrm_0.0.4.9000.zip(r-4.6)pmrm_0.0.4.9000.zip(r-4.5)
pmrm_0.0.4.9000.tgz(r-4.6-any)pmrm_0.0.4.9000.tgz(r-4.5-any)
pmrm_0.0.4.9000.tar.gz(r-4.7-any)pmrm_0.0.4.9000.tar.gz(r-4.6-any)
pmrm_0.0.4.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pmrm/json (API)

# Install 'pmrm' in R:
install.packages('pmrm', repos = c('https://openpharma.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/openpharma/pmrm/issues

Pkgdown/docs site:https://openpharma.github.io

On CRAN:

Conda:

adcompdisease-progression-modelmmrmpmrmrtmbtmb

5.80 score 6 stars 2 scripts 507 downloads 21 exports 33 dependencies

Last updated from:05b3fd1307. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK227
source / vignettesOK328
linux-release-x86_64OK223
macos-release-arm64OK152
macos-oldrel-arm64OK218
windows-develOK164
windows-releaseOK169
windows-oldrelOK153
wasm-releaseOK118

Exports:AICBICconfintdeviancefittedglancelogLikpmrm_estimatespmrm_marginalspmrm_model_decline_nonproportionalpmrm_model_decline_proportionalpmrm_model_slowing_nonproportionalpmrm_model_slowing_proportionalpmrm_simulate_decline_nonproportionalpmrm_simulate_decline_proportionalpmrm_simulate_slowing_nonproportionalpmrm_simulate_slowing_proportionalpredictresidualstidyVarCorr

Dependencies:clicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixnlmepillarpkgconfigR6RColorBrewerRcppRcppEigenrlangRTMBS7scalestibbletidyselectTMButf8vctrsviridisLitewithr

Validation
Implementation | Last run | Convergence | Parameter coverage
Rendered fromvalidation.Rmdusingknitr::rmarkdown

Last update: 2026-01-23
Started: 2026-01-23

Usage
Raw data | Models | Checking and troubleshooting | Summaries | Marginals | Predictions | Plots
Rendered fromusage.Rmdusingknitr::rmarkdown

Last update: 2026-01-22
Started: 2026-01-20

Models
Common elements | Data | Likelihood | Variance | Expected value of the control group | The decline models | The non-proportional decline model | The proportional decline model | The slowing models | The non-proportional slowing model | The proportional slowing model | References
Rendered frommodels.Rmdusingknitr::rmarkdown

Last update: 2026-01-21
Started: 2026-01-20

Readme and manuals

Help Manual

Help pageTopics
Akaike information criterion (AIC)AIC.pmrm_fit
Bayesian information criterion (BIC)BIC.pmrm_fit
Treatment effect parameterscoef.pmrm_fit
Confidence intervals of parametersconfint.pmrm_fit
Deviancedeviance.pmrm_fit
Fitted valuesfitted.pmrm_fit
Glance at a PMRM.glance.pmrm_fit
Extract the log likelihood.logLik.pmrm_fit
Plot a fitted PMRM.plot.pmrm_fit
Parameter estimates and confidence intervalspmrm_estimates
Marginal meanspmrm_marginals
Fit the non-proportional decline model.pmrm_model_decline_nonproportional
Fit the proportional decline model.pmrm_model_decline_proportional
Fit the non-proportional slowing model.pmrm_model_slowing_nonproportional
Fit the proportional slowing model.pmrm_model_slowing_proportional
Simulate non-proportional decline model.pmrm_simulate_decline_nonproportional
Simulate proportional decline model.pmrm_simulate_decline_proportional
Simulate non-proportional slowing model.pmrm_simulate_slowing_nonproportional
Simulate proportional slowing model.pmrm_simulate_slowing_proportional
Predict new outcomespredict.pmrm_fit
Print a fitted PMRM.print.pmrm_fit
'pmrm' residuals.residuals.pmrm_fit
Summarize a PMRM.summary.pmrm_fit
Tidy a fitted PMRM.tidy.pmrm_fit
Estimated covariance matrixVarCorr.pmrm_fit
Treatment effect parameter covariance matrixvcov.pmrm_fit