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, and count outcomes via 'Stan'. Also
provides simulated treatment comparison (STC) via parametric
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
deliberately mirrors multinma's so users can move between
ML-NMR and ML-UMR with the same workflow.
