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, ) 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.