mlumr: Multilevel Unanchored Meta-Regression for Indirect Treatment Comparisons

Bayesian multilevel unanchored meta-regression (ML-UMR) for indirect treatment comparisons using individual patient data (IPD) and aggregate data (AgD). Implements shared prognostic factor assumption (SPFA) and relaxed SPFA models for binary, continuous, count, and time-to-event (survival) outcomes via 'Stan', including parametric and flexible M-spline survival baselines. Also provides a one-arm simulated treatment comparison (STC) via comparator-population G-computation and naive unadjusted benchmarks. ML-UMR is an adaptation of the ML-NMR methodology (Phillippo et al. 2020, <doi:10.1111/rssa.12579>) implemented in the 'multinma' package (GPL-3) to the unanchored two-trial case. The public API uses familiar names for data setup, integration and effect summaries, but ML-NMR and ML-UMR specifications and fitted objects are not interchangeable because their evidence structures and models differ.

Version: 0.2.0
Depends: R (≥ 4.1.0)
Imports: methods, stats, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), rstantools (≥ 2.3.0), randtoolbox, survival (≥ 3.1-0), splines2 (≥ 0.4.1), ggplot2 (≥ 3.4.0)
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0)
Suggests: testthat (≥ 3.2.0), ggsurvfit, knitr, rmarkdown, posterior, bayesplot, loo, Matrix, cmdstanr, withr, flexsurv, detectseparation, xfun, R.rsp
Published: 2026-10-13
DOI: 10.32614/CRAN.package.mlumr
Author: Ahmad Sofi-Mahmudi ORCID iD [aut, cre], Conor Chandler ORCID iD [aut]
Maintainer: Ahmad Sofi-Mahmudi <a.sofimahmudi at gmail.com>
BugReports: https://github.com/choxos/mlumr/issues
License: GPL-3
URL: https://github.com/choxos/mlumr, https://choxos.github.io/mlumr/
NeedsCompilation: yes
SystemRequirements: GNU make
Additional_repositories: https://mc-stan.org/r-packages
Citation: mlumr citation info
Materials: NEWS
In views: MetaAnalysis
CRAN checks: mlumr results

Documentation:

Reference manual: mlumr.html , mlumr.pdf
Vignettes: Preparing data and integration points (source, R code)
Introduction to mlumr (source, R code)
Binary outcomes: an unanchored PASI 75 comparison (source)
Choosing a method for single-arm indirect comparisons: ML-UMR, STC, and naive (source)
Continuous outcomes: an unanchored mean-difference comparison (source)
Count outcomes: an unanchored rate-ratio comparison (source)
Fitting, priors, and diagnostics (source)
Aggregate subgroup information for the relaxed model (source)
Survival (time-to-event) outcomes (source)

Downloads:

Package source: mlumr_0.2.0.tar.gz
Windows binaries: r-devel: mlumr_0.1.0.zip, r-release: mlumr_0.1.0.zip, r-oldrel: mlumr_0.2.0.zip
macOS binaries: r-release (arm64): mlumr_0.2.0.tgz, r-oldrel (arm64): mlumr_0.2.0.tgz, r-release (x86_64): mlumr_0.2.0.tgz, r-oldrel (x86_64): mlumr_0.2.0.tgz
Old sources: mlumr archive

Linking:

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