What mlumr does
mlumr implements multilevel unanchored meta-regression (ML-UMR) for population-adjusted single-arm indirect treatment comparisons. It is for the situation where two treatments have never been compared in the same trial and there is no common comparator arm to anchor the comparison (single-arm trials, i.e., fully disconnected evidence), you hold individual patient data (IPD) for one treatment and only published aggregate data (AgD) for the other.
ML-UMR fits an outcome model to the IPD and integrates it over the comparator study’s covariate distribution, transporting the comparison to a decision-relevant population while adjusting for cross-trial covariate imbalance. It extends multilevel network meta-regression (ML-NMR) (Phillippo et al. 2020) to the unanchored, two-treatment case; the method and its assumptions are developed by Chandler and Ishak for binary (Chandler and Ishak 2025) and survival (Chandler and Ishak 2026) outcomes.
The current package is two-treatment: one index treatment (IPD) versus one comparator (AgD). Multi-treatment unanchored networks are out of scope.
ML-UMR should only be used for fully unanchored, single-arm
comparisons. The worked examples in these vignettes use data from
randomized trials, and to illustrate the method they create hypothetical
single-arm trials by dropping a common reference arm (or by treating a
trial’s randomized arms as separate sources), which leaves fully
disconnected evidence. In practice, randomized trials should never be
analyzed this way: ML-NMR (Phillippo et al. 2020) (for example
with the multinma package) or another appropriate method
should be used for them.
Supported outcome families
| Family | Outcome | Worked-example vignette |
|---|---|---|
| binomial | binary (0/1) response | vignette("binary-outcomes") |
| normal | continuous | vignette("continuous-outcomes") |
| poisson | counts with exposure | vignette("count-outcomes") |
| survival | time-to-event | vignette("survival-outcomes") |
The four methods
mlumr provides four estimators behind one data interface, so you can run them side by side as a sensitivity analysis:
| Method | Adjusts covariates? | Effect modification? | Inference |
|---|---|---|---|
| ML-UMR SPFA | yes | no (shared coefficients) | Bayesian |
| ML-UMR relaxed | yes | yes (treatment-specific) | Bayesian |
| STC | yes (outcome model) | no | frequentist |
| Naive | no | n/a | frequentist (benchmark) |
What “unanchored” does and does not buy you
Removing the common-arm requirement is what makes single-arm
comparisons possible, but it does not remove the need
for assumptions. Unanchored ML-UMR still relies on conditional
constancy of the absolute outcome: that, conditional on the
modelled covariates, the index outcome model transports to the
comparator population (and, under the default SPFA, that prognostic
effects are shared across treatments). These assumptions are strong and
largely untestable, so always report the naive benchmark and sensitivity
analyses (vignette("choosing-a-method")).
Installation
Install the released version from CRAN, or the development version from GitHub:
install.packages("mlumr") # from CRAN
# remotes::install_github("choxos/mlumr") # development versionThe workflow at a glance
Every analysis follows the same four steps, then a fit:
library(mlumr)
ipd <- set_ipd(...) # individual patient data (index)
agd <- set_agd(...) # aggregate data (comparator)
dat <- combine_data(ipd, agd) # combine
dat <- add_integration(dat, ...) # quasi-Monte Carlo integration points
fit <- mlumr(dat, model = "spfa") # fit the Bayesian modelThe exact set_ipd() / set_agd() arguments
depend on the outcome family; each per-outcome vignette shows a
complete, runnable example.
Where to go next
-
vignette("data-preparation"), the four-step pipeline and the numerical integration that powers population adjustment (covariate distributions, correlation, diagnostics). -
One outcome-family worked example,
vignette("binary-outcomes"),vignette("continuous-outcomes"),vignette("count-outcomes"), orvignette("survival-outcomes"). -
vignette("fitting-and-diagnostics"), sampler control, priors, and MCMC diagnostics. -
vignette("choosing-a-method"), assumptions, model comparison, and a decision guide for which estimate to report.