{ "_id": "6ace9712f6f39cd7e29a19d0", "Package": "mlumr", "Title": "Multilevel Unanchored Meta-Regression for Indirect Treatment\nComparisons", "Version": "0.2.0", "Authors@R": "c(\nperson(\"Ahmad\", \"Sofi-Mahmudi\", , \"a.sofimahmudi@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-6829-0823\")),\nperson(\"Conor\", \"Chandler\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-1365-9002\")))", "Description": "Bayesian multilevel unanchored meta-regression (ML-UMR)\nfor indirect treatment comparisons using individual patient\ndata (IPD) and aggregate data (AgD). Implements shared\nprognostic factor assumption (SPFA) and relaxed SPFA models for\nbinary, continuous, count, and time-to-event (survival)\noutcomes via 'Stan', including parametric and flexible M-spline\nsurvival baselines. Also provides a one-arm simulated treatment\ncomparison (STC) via comparator-population G-computation and\nnaive unadjusted benchmarks. ML-UMR is an adaptation of the\nML-NMR methodology (Phillippo et al. 2020,\n) implemented in the 'multinma' package\n(GPL-3) to the unanchored two-trial case. 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