mlumr/codemeta.json at main · choxos/mlumr · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
{
  "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
  "@type": "SoftwareSourceCode",
  "@id": "https://github.com/choxos/mlumr",
  "name": "mlumr",
  "description": "Bayesian multilevel unanchored meta-regression (ML-UMR) for indirect treatment comparisons using individual patient data (IPD) and aggregate data (AgD). Implements the shared prognostic factor assumption (SPFA) and relaxed SPFA models for binary, continuous, count, and time-to-event (survival) outcomes via Stan, including parametric and flexible M-spline survival baselines, with transport of marginal treatment effects to arbitrary target populations by Bayesian g-computation (covariate-conditional effects are reported at specified profiles, not population-standardized). Also provides simulated treatment comparison (STC) via parametric G-computation and naive unadjusted benchmarks. ML-UMR is an adaptation of the multilevel network meta-regression (ML-NMR) methodology of Phillippo et al. (2020), implemented in the multinma package, to the unanchored disconnected-evidence setting.",
  "codeRepository": "https://github.com/choxos/mlumr",
  "url": "https://choxos.github.io/mlumr/",
  "issueTracker": "https://github.com/choxos/mlumr/issues",
  "license": "https://spdx.org/licenses/GPL-3.0-only.html",
  "version": "0.2.0",
  "softwareVersion": "0.2.0",
  "programmingLanguage": "R",
  "runtimePlatform": "R >= 4.1.0",
  "operatingSystem": "OS Independent",
  "applicationCategory": "Indirect treatment comparison / evidence synthesis",
  "developmentStatus": "active",
  "keywords": [
    "indirect treatment comparison",
    "population-adjusted indirect comparison",
    "network meta-analysis",
    "health technology assessment",
    "evidence synthesis",
    "Bayesian",
    "Stan",
    "survival analysis",
    "multilevel network meta-regression",
    "R"
  ],
  "softwareRequirements": [
    "R >= 4.1.0",
    "methods",
    "stats",
    "Rcpp",
    "RcppParallel",
    "rstan",
    "rstantools",
    "randtoolbox",
    "copula",
    "splines2",
    "survival",
    "ggplot2"
  ],
  "author": [
    {
      "@type": "Person",
      "givenName": "Ahmad",
      "familyName": "Sofi-Mahmudi",
      "email": "a.sofimahmudi@gmail.com",
      "@id": "https://orcid.org/0000-0001-6829-0823"
    },
    {
      "@type": "Person",
      "givenName": "Conor",
      "familyName": "Chandler",
      "@id": "https://orcid.org/0000-0002-1365-9002"
    }
  ],
  "maintainer": [
    {
      "@type": "Person",
      "givenName": "Ahmad",
      "familyName": "Sofi-Mahmudi",
      "email": "a.sofimahmudi@gmail.com",
      "@id": "https://orcid.org/0000-0001-6829-0823"
    }
  ],
  "contributor": [
    {
      "@type": "Role",
      "roleName": [
        "Software",
        "Methodology",
        "Validation",
        "Writing - original draft",
        "Project administration"
      ],
      "url": "https://credit.niso.org/",
      "schema:author": {
        "@id": "https://orcid.org/0000-0001-6829-0823",
        "name": "Ahmad Sofi-Mahmudi"
      }
    },
    {
      "@type": "Role",
      "roleName": [
        "Conceptualization",
        "Methodology",
        "Writing - review & editing"
      ],
      "url": "https://credit.niso.org/",
      "schema:author": {
        "@id": "https://orcid.org/0000-0002-1365-9002",
        "name": "Conor Chandler"
      }
    }
  ],
  "relatedLink": [
    "https://choxos.github.io/mlumr/",
    "https://CRAN.R-project.org/package=mlumr"
  ],
  "isBasedOn": "https://doi.org/10.5281/zenodo.3904454",
  "referencePublication": "https://doi.org/10.1111/rssa.12579"
}