Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-12

AUTHORS

Michael D Coory, Rachael A Wills, Adrian G Barnett

ABSTRACT

BACKGROUND: The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. METHODS: This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. RESULTS: Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. CONCLUSION: In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit. More... »

PAGES

30

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2288-9-30

DOI

http://dx.doi.org/10.1186/1471-2288-9-30

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052310482

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19426561


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adolescent", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Australia", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bayes Theorem", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Biometry", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cluster Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Public Health", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Assessment", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Young Adult", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Queensland", 
          "id": "https://www.grid.ac/institutes/grid.1003.2", 
          "name": [
            "School of Population Health, Mayne Medical School, University of Queensland, Herston, Australia", 
            "Statistical Analysis Unit, Queensland Department of Health, Brisbane, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Coory", 
        "givenName": "Michael D", 
        "id": "sg:person.01053520214.71", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01053520214.71"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Statistical Analysis Unit, Queensland Department of Health, Brisbane, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wills", 
        "givenName": "Rachael A", 
        "id": "sg:person.01143114103.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143114103.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Queensland University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.1024.7", 
          "name": [
            "School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Barnett", 
        "givenName": "Adrian G", 
        "id": "sg:person.012524527342.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012524527342.78"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1081/bip-200067922", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000226103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1081/bip-200067922", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000226103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-985x.00181", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008151763"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-985x.00181", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008151763"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1289/ehp.9021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013593535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.291.20.2457", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018067873"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pst.303", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024034979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.0006-341x.2000.00922.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024757000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ije/dyi312", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030886333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00001648-199107000-00002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031123925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00001648-199107000-00002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031123925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1008929526011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031552564", 
          "https://doi.org/10.1023/a:1008929526011"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0140-6736(91)90201-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034838034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0140-6736(91)90201-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034838034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.310.6973.170", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039852171"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3322/canjclin.54.5.273", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044050125"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0378-3758(99)00044-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051842305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/84.2.419", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059420753"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1074524007", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5694/j.1326-5377.1999.tb123593.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074524007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5694/j.1326-5377.1999.tb123593.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074524007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5694/j.1326-5377.1999.tb123593.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074524007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.aje.a115787", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078403988"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.aje.a115507", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078620379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.aje.a115621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078714259"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.aje.a115776", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078714307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.aje.a115790", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078714319"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1079302930", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1079741044", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/acprof:oso/9780198509882.001.0001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098721547"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/9780470035771", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106885403"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-12", 
    "datePublishedReg": "2009-12-01", 
    "description": "BACKGROUND: The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods.\nMETHODS: This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution.\nRESULTS: Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective \"guesstimate\". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior.\nCONCLUSION: In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2288-9-30", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1024940", 
        "issn": [
          "1471-2288"
        ], 
        "name": "BMC Medical Research Methodology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department", 
    "pagination": "30", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f92c2b18f30d42bd8bdc4c9109d31a431fd98a8cdf597da4c53fdf12db4e8dc0"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "19426561"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100968545"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2288-9-30"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1052310482"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2288-9-30", 
      "https://app.dimensions.ai/details/publication/pub.1052310482"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T14:22", 
    "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_8660_00000596.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2F1471-2288-9-30"
  }
]
 

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.10.1186/1471-2288-9-30'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2288-9-30'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2288-9-30'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2288-9-30'


 

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

217 TRIPLES      21 PREDICATES      68 URIs      35 LITERALS      23 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2288-9-30 schema:about N0f491b3004ae47cf98b5464ff2312f4a
2 N50206cfb096a4e9eaa3ed893dee19a7d
3 N542f504606c74c92a16a9a10237142ee
4 N59266b60ffb245728a3fbe2e4614a7e1
5 N6ad2e45a3ddf470a892a0a343640537e
6 N79c33422fa2148ad8a1d0e1b1b0707c4
7 N8a1a3f99c4914f42ad1b0e9ee70cd83c
8 Na1f5ef1116b24aaa910e522a39c4b4f0
9 Na5c49802d79c4794a77b1defe2840e80
10 Nb4b3aed8a2964c6ea59766fea93cc83c
11 Nd13f3d1cccc64a42ba68996e9cae47b5
12 Ne115733563274478becfd0cbdeb6cad2
13 Ne552026097614cd6b95dae72eaf2f2ce
14 Nead1a2049bbb42738a8f06ff60b1c34a
15 anzsrc-for:01
16 anzsrc-for:0104
17 schema:author Nadad9f4b827d4bad9bfc187bbd45ef70
18 schema:citation sg:pub.10.1023/a:1008929526011
19 https://app.dimensions.ai/details/publication/pub.1074524007
20 https://app.dimensions.ai/details/publication/pub.1079302930
21 https://app.dimensions.ai/details/publication/pub.1079741044
22 https://doi.org/10.1001/jama.291.20.2457
23 https://doi.org/10.1002/9780470035771
24 https://doi.org/10.1002/pst.303
25 https://doi.org/10.1016/0140-6736(91)90201-y
26 https://doi.org/10.1016/s0378-3758(99)00044-0
27 https://doi.org/10.1081/bip-200067922
28 https://doi.org/10.1093/acprof:oso/9780198509882.001.0001
29 https://doi.org/10.1093/biomet/84.2.419
30 https://doi.org/10.1093/ije/dyi312
31 https://doi.org/10.1093/oxfordjournals.aje.a115507
32 https://doi.org/10.1093/oxfordjournals.aje.a115621
33 https://doi.org/10.1093/oxfordjournals.aje.a115776
34 https://doi.org/10.1093/oxfordjournals.aje.a115787
35 https://doi.org/10.1093/oxfordjournals.aje.a115790
36 https://doi.org/10.1097/00001648-199107000-00002
37 https://doi.org/10.1111/1467-985x.00181
38 https://doi.org/10.1111/j.0006-341x.2000.00922.x
39 https://doi.org/10.1136/bmj.310.6973.170
40 https://doi.org/10.1289/ehp.9021
41 https://doi.org/10.3322/canjclin.54.5.273
42 https://doi.org/10.5694/j.1326-5377.1999.tb123593.x
43 schema:datePublished 2009-12
44 schema:datePublishedReg 2009-12-01
45 schema:description BACKGROUND: The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. METHODS: This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. RESULTS: Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. CONCLUSION: In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.
46 schema:genre research_article
47 schema:inLanguage en
48 schema:isAccessibleForFree true
49 schema:isPartOf N458d182f9ede493b9c29948b625b5b64
50 N7bf9d38261fd46ef97ef9b03f6832fba
51 sg:journal.1024940
52 schema:name Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department
53 schema:pagination 30
54 schema:productId N12aff881b97f427eb9a07d888d283b4f
55 N307b06945745492c9d42444efd592192
56 N6e376916465c4a438e7562b44b63a0be
57 Nb3717673a81446dab063434e089a78eb
58 Nf8e3c572b98a4265a0cd7ae864fdc3f4
59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052310482
60 https://doi.org/10.1186/1471-2288-9-30
61 schema:sdDatePublished 2019-04-10T14:22
62 schema:sdLicense https://scigraph.springernature.com/explorer/license/
63 schema:sdPublisher N6f77933515384d90987b8bc1dae8a416
64 schema:url http://link.springer.com/10.1186%2F1471-2288-9-30
65 sgo:license sg:explorer/license/
66 sgo:sdDataset articles
67 rdf:type schema:ScholarlyArticle
68 N0f491b3004ae47cf98b5464ff2312f4a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
69 schema:name Biometry
70 rdf:type schema:DefinedTerm
71 N12aff881b97f427eb9a07d888d283b4f schema:name dimensions_id
72 schema:value pub.1052310482
73 rdf:type schema:PropertyValue
74 N2d70a70517794c11bf84f5a8d1b51354 rdf:first sg:person.012524527342.78
75 rdf:rest rdf:nil
76 N307b06945745492c9d42444efd592192 schema:name readcube_id
77 schema:value f92c2b18f30d42bd8bdc4c9109d31a431fd98a8cdf597da4c53fdf12db4e8dc0
78 rdf:type schema:PropertyValue
79 N458d182f9ede493b9c29948b625b5b64 schema:volumeNumber 9
80 rdf:type schema:PublicationVolume
81 N50206cfb096a4e9eaa3ed893dee19a7d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
82 schema:name Bayes Theorem
83 rdf:type schema:DefinedTerm
84 N542f504606c74c92a16a9a10237142ee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
85 schema:name Reproducibility of Results
86 rdf:type schema:DefinedTerm
87 N59266b60ffb245728a3fbe2e4614a7e1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Middle Aged
89 rdf:type schema:DefinedTerm
90 N622412fbec2d4c5c8e6a696b46c6523e rdf:first sg:person.01143114103.07
91 rdf:rest N2d70a70517794c11bf84f5a8d1b51354
92 N6ad2e45a3ddf470a892a0a343640537e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Risk Assessment
94 rdf:type schema:DefinedTerm
95 N6e376916465c4a438e7562b44b63a0be schema:name pubmed_id
96 schema:value 19426561
97 rdf:type schema:PropertyValue
98 N6f77933515384d90987b8bc1dae8a416 schema:name Springer Nature - SN SciGraph project
99 rdf:type schema:Organization
100 N79c33422fa2148ad8a1d0e1b1b0707c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name Female
102 rdf:type schema:DefinedTerm
103 N7bf9d38261fd46ef97ef9b03f6832fba schema:issueNumber 1
104 rdf:type schema:PublicationIssue
105 N8a1a3f99c4914f42ad1b0e9ee70cd83c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
106 schema:name Humans
107 rdf:type schema:DefinedTerm
108 Na1f5ef1116b24aaa910e522a39c4b4f0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Public Health
110 rdf:type schema:DefinedTerm
111 Na5c49802d79c4794a77b1defe2840e80 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Adult
113 rdf:type schema:DefinedTerm
114 Nadad9f4b827d4bad9bfc187bbd45ef70 rdf:first sg:person.01053520214.71
115 rdf:rest N622412fbec2d4c5c8e6a696b46c6523e
116 Nb3717673a81446dab063434e089a78eb schema:name nlm_unique_id
117 schema:value 100968545
118 rdf:type schema:PropertyValue
119 Nb4b3aed8a2964c6ea59766fea93cc83c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Adolescent
121 rdf:type schema:DefinedTerm
122 Nd13f3d1cccc64a42ba68996e9cae47b5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Neoplasms
124 rdf:type schema:DefinedTerm
125 Ndd3cb033dd0b4dd9930d05c3af5dd60d schema:name Statistical Analysis Unit, Queensland Department of Health, Brisbane, Australia
126 rdf:type schema:Organization
127 Ne115733563274478becfd0cbdeb6cad2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Cluster Analysis
129 rdf:type schema:DefinedTerm
130 Ne552026097614cd6b95dae72eaf2f2ce schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Australia
132 rdf:type schema:DefinedTerm
133 Nead1a2049bbb42738a8f06ff60b1c34a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Young Adult
135 rdf:type schema:DefinedTerm
136 Nf8e3c572b98a4265a0cd7ae864fdc3f4 schema:name doi
137 schema:value 10.1186/1471-2288-9-30
138 rdf:type schema:PropertyValue
139 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
140 schema:name Mathematical Sciences
141 rdf:type schema:DefinedTerm
142 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
143 schema:name Statistics
144 rdf:type schema:DefinedTerm
145 sg:journal.1024940 schema:issn 1471-2288
146 schema:name BMC Medical Research Methodology
147 rdf:type schema:Periodical
148 sg:person.01053520214.71 schema:affiliation https://www.grid.ac/institutes/grid.1003.2
149 schema:familyName Coory
150 schema:givenName Michael D
151 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01053520214.71
152 rdf:type schema:Person
153 sg:person.01143114103.07 schema:affiliation Ndd3cb033dd0b4dd9930d05c3af5dd60d
154 schema:familyName Wills
155 schema:givenName Rachael A
156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143114103.07
157 rdf:type schema:Person
158 sg:person.012524527342.78 schema:affiliation https://www.grid.ac/institutes/grid.1024.7
159 schema:familyName Barnett
160 schema:givenName Adrian G
161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012524527342.78
162 rdf:type schema:Person
163 sg:pub.10.1023/a:1008929526011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031552564
164 https://doi.org/10.1023/a:1008929526011
165 rdf:type schema:CreativeWork
166 https://app.dimensions.ai/details/publication/pub.1074524007 schema:CreativeWork
167 https://app.dimensions.ai/details/publication/pub.1079302930 schema:CreativeWork
168 https://app.dimensions.ai/details/publication/pub.1079741044 schema:CreativeWork
169 https://doi.org/10.1001/jama.291.20.2457 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018067873
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1002/9780470035771 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106885403
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1002/pst.303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024034979
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/0140-6736(91)90201-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1034838034
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/s0378-3758(99)00044-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051842305
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1081/bip-200067922 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000226103
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1093/acprof:oso/9780198509882.001.0001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098721547
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1093/biomet/84.2.419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059420753
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1093/ije/dyi312 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030886333
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1093/oxfordjournals.aje.a115507 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078620379
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1093/oxfordjournals.aje.a115621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078714259
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1093/oxfordjournals.aje.a115776 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078714307
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1093/oxfordjournals.aje.a115787 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078403988
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1093/oxfordjournals.aje.a115790 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078714319
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1097/00001648-199107000-00002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031123925
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1111/1467-985x.00181 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008151763
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1111/j.0006-341x.2000.00922.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1024757000
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1136/bmj.310.6973.170 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039852171
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1289/ehp.9021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013593535
206 rdf:type schema:CreativeWork
207 https://doi.org/10.3322/canjclin.54.5.273 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044050125
208 rdf:type schema:CreativeWork
209 https://doi.org/10.5694/j.1326-5377.1999.tb123593.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1074524007
210 rdf:type schema:CreativeWork
211 https://www.grid.ac/institutes/grid.1003.2 schema:alternateName University of Queensland
212 schema:name School of Population Health, Mayne Medical School, University of Queensland, Herston, Australia
213 Statistical Analysis Unit, Queensland Department of Health, Brisbane, Australia
214 rdf:type schema:Organization
215 https://www.grid.ac/institutes/grid.1024.7 schema:alternateName Queensland University of Technology
216 schema:name School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia
217 rdf:type schema:Organization
 




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


...