A Cautionary Note on the Use of the Grønnesby and Borgan Goodness-of-Fit Test for the Cox Proportional Hazards Model View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-09

AUTHORS

Susanne May, David W. Hosmer

ABSTRACT

Grønnesby and Borgan (1996, Lifetime Data Analysis 2, 315-328) propose an omnibus goodness-of-fit test for the Cox proportional hazards model. The test is based on grouping the subjects by their estimated risk score and comparing the number of observed and a model based estimated number of expected events within each group. We show, using extensive simulations, that even for moderate sample sizes the choice of number of groups is critical for the test to attain the specified size. In light of these results we suggest a grouping strategy under which the test attains the correct size even for small samples. The power of the test statistic seems to be acceptable when compared to other goodness-of-fit tests. More... »

PAGES

283-291

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/b:lida.0000036393.29224.1d

DOI

http://dx.doi.org/10.1023/b:lida.0000036393.29224.1d

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027297426

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15456108


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Proportional Hazards Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sample Size", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Survival Analysis", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of California, San Diego", 
          "id": "https://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Division of Biostatistics, Department of Family and Preventive Medicine and Department of Neurosciences, University of California, San Diego, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "May", 
        "givenName": "Susanne", 
        "id": "sg:person.010665722747.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010665722747.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Massachusetts Amherst", 
          "id": "https://www.grid.ac/institutes/grid.266683.f", 
          "name": [
            "Department of Biostatistics and Epidemiology, University of Massachusetts at Amherst, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hosmer", 
        "givenName": "David W.", 
        "id": "sg:person.01176032357.96", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176032357.96"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1023/a:1009686119993", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004744288", 
          "https://doi.org/10.1023/a:1009686119993"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1009612305785", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018550313", 
          "https://doi.org/10.1023/a:1009612305785"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.0006-341x.1999.00580.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044935152"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00127305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045452961", 
          "https://doi.org/10.1007/bf00127305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00127305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045452961", 
          "https://doi.org/10.1007/bf00127305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1996.10476725", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058305008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/81.3.515", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059420497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/000313001750358491", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064196893"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004-09", 
    "datePublishedReg": "2004-09-01", 
    "description": "Gr\u00f8nnesby and Borgan (1996, Lifetime Data Analysis 2, 315-328) propose an omnibus goodness-of-fit test for the Cox proportional hazards model. The test is based on grouping the subjects by their estimated risk score and comparing the number of observed and a model based estimated number of expected events within each group. We show, using extensive simulations, that even for moderate sample sizes the choice of number of groups is critical for the test to attain the specified size. In light of these results we suggest a grouping strategy under which the test attains the correct size even for small samples. The power of the test statistic seems to be acceptable when compared to other goodness-of-fit tests.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1023/b:lida.0000036393.29224.1d", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1114760", 
        "issn": [
          "1380-7870", 
          "1572-9249"
        ], 
        "name": "Lifetime Data Analysis", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "10"
      }
    ], 
    "name": "A Cautionary Note on the Use of the Gr\u00f8nnesby and Borgan Goodness-of-Fit Test for the Cox Proportional Hazards Model", 
    "pagination": "283-291", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "82092bde44bb42db1343bd0f091921a06c0acde82d3cf22ef2f67c2bc1edc67f"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "15456108"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9516348"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1023/b:lida.0000036393.29224.1d"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027297426"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1023/b:lida.0000036393.29224.1d", 
      "https://app.dimensions.ai/details/publication/pub.1027297426"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T13:14", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8659_00000505.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1023%2FB%3ALIDA.0000036393.29224.1d"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1023/b:lida.0000036393.29224.1d'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/b:lida.0000036393.29224.1d'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/b:lida.0000036393.29224.1d'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/b:lida.0000036393.29224.1d'


 

This table displays all metadata directly associated to this object as RDF triples.

123 TRIPLES      21 PREDICATES      41 URIs      26 LITERALS      14 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1023/b:lida.0000036393.29224.1d schema:about N1a1fe703ad14426c994511cdfba86972
2 N2020d5a318424f899b94e02c69852944
3 N5646d4b8188a4b5c9435ff992079e673
4 N57a452f992b54a17a1223cba06b4a253
5 Nc5ddc9f6aa394bd2a2e285e2fa3ed694
6 anzsrc-for:11
7 anzsrc-for:1117
8 schema:author N485c83f884aa43bdaf2b1f56931b9bf9
9 schema:citation sg:pub.10.1007/bf00127305
10 sg:pub.10.1023/a:1009612305785
11 sg:pub.10.1023/a:1009686119993
12 https://doi.org/10.1080/01621459.1996.10476725
13 https://doi.org/10.1093/biomet/81.3.515
14 https://doi.org/10.1111/j.0006-341x.1999.00580.x
15 https://doi.org/10.1198/000313001750358491
16 schema:datePublished 2004-09
17 schema:datePublishedReg 2004-09-01
18 schema:description Grønnesby and Borgan (1996, Lifetime Data Analysis 2, 315-328) propose an omnibus goodness-of-fit test for the Cox proportional hazards model. The test is based on grouping the subjects by their estimated risk score and comparing the number of observed and a model based estimated number of expected events within each group. We show, using extensive simulations, that even for moderate sample sizes the choice of number of groups is critical for the test to attain the specified size. In light of these results we suggest a grouping strategy under which the test attains the correct size even for small samples. The power of the test statistic seems to be acceptable when compared to other goodness-of-fit tests.
19 schema:genre research_article
20 schema:inLanguage en
21 schema:isAccessibleForFree false
22 schema:isPartOf N63439dd309aa4ef9956d85287e16fa9b
23 Nff18aecc2bc542e4a18bee28da4e201a
24 sg:journal.1114760
25 schema:name A Cautionary Note on the Use of the Grønnesby and Borgan Goodness-of-Fit Test for the Cox Proportional Hazards Model
26 schema:pagination 283-291
27 schema:productId N31070476018741499e0e441f1a2e8a70
28 N3ab4e7e14937408e97e2229a92a9dff8
29 N42c945a59b014547b4ace2afbc9ff8f6
30 N5ab104e92ef848b49844da530b6f48c4
31 Nf15e35dd938645f8bbd4856e6932d5ee
32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027297426
33 https://doi.org/10.1023/b:lida.0000036393.29224.1d
34 schema:sdDatePublished 2019-04-10T13:14
35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
36 schema:sdPublisher Nc2870d25ebc4434987fe10237ed6a9b9
37 schema:url http://link.springer.com/10.1023%2FB%3ALIDA.0000036393.29224.1d
38 sgo:license sg:explorer/license/
39 sgo:sdDataset articles
40 rdf:type schema:ScholarlyArticle
41 N1a1fe703ad14426c994511cdfba86972 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
42 schema:name Proportional Hazards Models
43 rdf:type schema:DefinedTerm
44 N2020d5a318424f899b94e02c69852944 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
45 schema:name Risk
46 rdf:type schema:DefinedTerm
47 N31070476018741499e0e441f1a2e8a70 schema:name dimensions_id
48 schema:value pub.1027297426
49 rdf:type schema:PropertyValue
50 N3ab4e7e14937408e97e2229a92a9dff8 schema:name doi
51 schema:value 10.1023/b:lida.0000036393.29224.1d
52 rdf:type schema:PropertyValue
53 N42c945a59b014547b4ace2afbc9ff8f6 schema:name pubmed_id
54 schema:value 15456108
55 rdf:type schema:PropertyValue
56 N485c83f884aa43bdaf2b1f56931b9bf9 rdf:first sg:person.010665722747.76
57 rdf:rest Nae2eb3a71fda4c639556a378c8d9d4d2
58 N5646d4b8188a4b5c9435ff992079e673 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
59 schema:name Humans
60 rdf:type schema:DefinedTerm
61 N57a452f992b54a17a1223cba06b4a253 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
62 schema:name Survival Analysis
63 rdf:type schema:DefinedTerm
64 N5ab104e92ef848b49844da530b6f48c4 schema:name readcube_id
65 schema:value 82092bde44bb42db1343bd0f091921a06c0acde82d3cf22ef2f67c2bc1edc67f
66 rdf:type schema:PropertyValue
67 N63439dd309aa4ef9956d85287e16fa9b schema:issueNumber 3
68 rdf:type schema:PublicationIssue
69 Nae2eb3a71fda4c639556a378c8d9d4d2 rdf:first sg:person.01176032357.96
70 rdf:rest rdf:nil
71 Nc2870d25ebc4434987fe10237ed6a9b9 schema:name Springer Nature - SN SciGraph project
72 rdf:type schema:Organization
73 Nc5ddc9f6aa394bd2a2e285e2fa3ed694 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
74 schema:name Sample Size
75 rdf:type schema:DefinedTerm
76 Nf15e35dd938645f8bbd4856e6932d5ee schema:name nlm_unique_id
77 schema:value 9516348
78 rdf:type schema:PropertyValue
79 Nff18aecc2bc542e4a18bee28da4e201a schema:volumeNumber 10
80 rdf:type schema:PublicationVolume
81 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
82 schema:name Medical and Health Sciences
83 rdf:type schema:DefinedTerm
84 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
85 schema:name Public Health and Health Services
86 rdf:type schema:DefinedTerm
87 sg:journal.1114760 schema:issn 1380-7870
88 1572-9249
89 schema:name Lifetime Data Analysis
90 rdf:type schema:Periodical
91 sg:person.010665722747.76 schema:affiliation https://www.grid.ac/institutes/grid.266100.3
92 schema:familyName May
93 schema:givenName Susanne
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010665722747.76
95 rdf:type schema:Person
96 sg:person.01176032357.96 schema:affiliation https://www.grid.ac/institutes/grid.266683.f
97 schema:familyName Hosmer
98 schema:givenName David W.
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176032357.96
100 rdf:type schema:Person
101 sg:pub.10.1007/bf00127305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045452961
102 https://doi.org/10.1007/bf00127305
103 rdf:type schema:CreativeWork
104 sg:pub.10.1023/a:1009612305785 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018550313
105 https://doi.org/10.1023/a:1009612305785
106 rdf:type schema:CreativeWork
107 sg:pub.10.1023/a:1009686119993 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004744288
108 https://doi.org/10.1023/a:1009686119993
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1080/01621459.1996.10476725 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058305008
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1093/biomet/81.3.515 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059420497
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1111/j.0006-341x.1999.00580.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1044935152
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1198/000313001750358491 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064196893
117 rdf:type schema:CreativeWork
118 https://www.grid.ac/institutes/grid.266100.3 schema:alternateName University of California, San Diego
119 schema:name Division of Biostatistics, Department of Family and Preventive Medicine and Department of Neurosciences, University of California, San Diego, CA, USA
120 rdf:type schema:Organization
121 https://www.grid.ac/institutes/grid.266683.f schema:alternateName University of Massachusetts Amherst
122 schema:name Department of Biostatistics and Epidemiology, University of Massachusetts at Amherst, MA, USA
123 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...