# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "mlumr" in publications use:' type: software license: GPL-3.0-only title: 'mlumr: Multilevel Unanchored Meta-Regression for Indirect Treatment Comparisons' version: 0.1.0 abstract: 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. authors: - family-names: Sofi-Mahmudi given-names: Ahmad email: a.sofimahmudi@gmail.com orcid: https://orcid.org/0000-0001-6829-0823 - family-names: Chandler given-names: Conor orcid: https://orcid.org/0000-0002-1365-9002 preferred-citation: type: manual title: 'mlumr: Multilevel Unanchored Meta-Regression for Indirect Treatment Comparisons' authors: - family-names: Sofi-Mahmudi given-names: Ahmad orcid: https://orcid.org/0000-0001-6829-0823 email: a.sofimahmudi@gmail.com - family-names: Chandler given-names: Conor orcid: https://orcid.org/0000-0002-1365-9002 year: '2026' notes: R package version 0.1.0. An adaptation of the ML-NMR methodology to the unanchored case; built on conventions established by the 'multinma' package (Phillippo et al., GPL-3). url: https://github.com/choxos/mlumr repository: https://cran.r-universe.dev repository-code: https://github.com/choxos/mlumr commit: 5a8d1ccb0a47949e856f0c2f952361a8f45285c2 url: https://choxos.github.io/mlumr/ date-released: '2026-05-20' contact: - family-names: Sofi-Mahmudi given-names: Ahmad email: a.sofimahmudi@gmail.com orcid: https://orcid.org/0000-0001-6829-0823 references: - type: manual title: 'multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data' authors: - family-names: Phillippo given-names: David M. orcid: https://orcid.org/0000-0003-2672-7841 year: '2020' notes: R package, GPL-3. The ML-NMR reference implementation that mlumr adapts to the unanchored case. url: https://dmphillippo.github.io/multinma/ doi: 10.5281/zenodo.3904454 - type: article title: 'Anchors Away: Navigating Unanchored Indirect Comparisons With Multilevel Unanchored Meta-Regression (ML-UMR)' authors: - family-names: Chandler given-names: Conor - family-names: Ishak given-names: Jack journal: Value in Health year: '2025' volume: '28' notes: ISPOR Europe 2025 poster, MSR28 url: https://www.valueinhealthjournal.com/article/S1098-3015(25)05944-3/abstract start: S498 - type: article title: 'Surviving Unanchored Indirect Comparisons: An Extension of Multilevel Unanchored Meta-Regression (ML-UMR) for Survival Analyses' authors: - family-names: Chandler given-names: Conor - family-names: Ishak given-names: Jack journal: Value in Health year: '2026' volume: '29' issue: S6 notes: ISPOR 2026 poster, MSR131 url: https://www.ispor.org/heor-resources/presentations-database/presentation-cti/ispor-2026/poster-session-3-3/surviving-unanchored-indirect-comparisons-an-extension-of-multilevel-unanchored-meta-regression-ml-umr-for-survival-analyses - type: article title: Multilevel Network Meta-Regression for population-adjusted treatment comparisons authors: - family-names: Phillippo given-names: David M. - family-names: Dias given-names: Sofia - family-names: Ades given-names: A. E. - family-names: Belger given-names: Mark - family-names: Brnabic given-names: Alan - family-names: Schacht given-names: Alexander - family-names: Saure given-names: Daniel - family-names: Kadziola given-names: Zbigniew - family-names: Welton given-names: Nicky J. journal: 'Journal of the Royal Statistical Society: Series A' year: '2020' volume: '183' issue: '3' doi: 10.1111/rssa.12579 start: '1189' end: '1210'