Choice of exposure scores for categorical regression in meta-analysis: a case study of a common problem View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2005-05

AUTHORS

Dora Il’yasova, Irva Hertz-Picciotto, Ulrike Peters, Jesse A. Berlin, Charles Poole

ABSTRACT

OBJECTIVE: Reporting categorical relative risk estimates for a series of exposure levels versus a common reference category is a widespread practice. In meta-analysis, categorical regression estimates a dose-response trend from such results. This method requires the assignment of a single score to each exposure category. We examined how closely meta-analytical categorical regression approximates the results of analysis based on the individual-level continuous exposure. METHODS: The analysis included five studies on tea intake and outcomes related to colorectal cancer. In addition, we derived categorical mean and median values from published distributions of tea consumption in similar populations to assign scores to the categories of tea intake when possible. We examined whether these derived mean and median values well approximate the individual-level results. RESULTS: In meta-analytical categorical regression, using the midrange scores approximated the individual-level continuous analyses reasonably well, if the value assigned to the uppermost, open-ended category was at least as high as the lower bound plus the width of the second-highest category. Categorical mean values derived from the published distributions of regular tea (in the US) and green tea (in Japan) well approximated the slope obtained from individual-level analysis. CONCLUSION: Publication of both the categorical and the continuous estimates of effect in primary studies, with their standard errors, can enhance the quality of meta-analysis, as well as providing intrinsically valuable information on dose-response. More... »

PAGES

383-388

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10552-004-5025-x

DOI

http://dx.doi.org/10.1007/s10552-004-5025-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031787925

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15953980


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": "Age Distribution", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cohort Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Colorectal Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Confidence Intervals", 
        "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": "Incidence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Odds Ratio", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Poisson Distribution", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Primary Prevention", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Regression Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Assessment", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sex Distribution", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tea", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Time Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "United States", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of North Carolina System", 
          "id": "https://www.grid.ac/institutes/grid.410711.2", 
          "name": [
            "Department Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA", 
            "Medical Center Boulevard, Wake Forest University School of Medicine, 27157, Winston-Salem, NC"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Il\u2019yasova", 
        "givenName": "Dora", 
        "id": "sg:person.01007456337.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007456337.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California, Davis", 
          "id": "https://www.grid.ac/institutes/grid.27860.3b", 
          "name": [
            "Department Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA", 
            "Department of Epidemiology and Preventive Medicine, University of California, Davis, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hertz-Picciotto", 
        "givenName": "Irva", 
        "id": "sg:person.0775617774.23", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0775617774.23"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Institute of Arthritis and Musculoskeletal and Skin Diseases", 
          "id": "https://www.grid.ac/institutes/grid.420086.8", 
          "name": [
            "Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Bethesda, Maryland, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Peters", 
        "givenName": "Ulrike", 
        "id": "sg:person.01033315763.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01033315763.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Pennsylvania", 
          "id": "https://www.grid.ac/institutes/grid.25879.31", 
          "name": [
            "Center for Clinical Epidemiology and Biostatistics and Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Berlin", 
        "givenName": "Jesse A.", 
        "id": "sg:person.0673476060.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0673476060.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of North Carolina System", 
          "id": "https://www.grid.ac/institutes/grid.410711.2", 
          "name": [
            "Department Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Poole", 
        "givenName": "Charles", 
        "id": "sg:person.01314504407.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01314504407.65"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/oxfordjournals.aje.a010035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000007454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.0006-341x.2001.00671.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002607343"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0895-4356(91)90158-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003158896"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ijc.10126", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009686761"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ije/31.1.59", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011259635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/aje/154.6.495", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011558881"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1079/phn2001314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017460353"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/humrep/17.9.2307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018539626"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sim.1595", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025416390"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0895-4356(96)00297-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025852750"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1054/bjoc.2001.2040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026630048", 
          "https://doi.org/10.1054/bjoc.2001.2040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1054/bjoc.2001.2040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026630048", 
          "https://doi.org/10.1054/bjoc.2001.2040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/aje/kwh142", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026635931"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.289.5.579", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027136581"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ijc.2910410510", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035465216"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00001648-200105000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043322471"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00001648-200105000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043322471"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00001648-199305000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044526465"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00001648-199305000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044526465"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1047-2797(02)00459-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052121883"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1047-2797(02)00459-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052121883"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jech.57.3.166", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052429031"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1289/ehp.99107s6885", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064747540"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2105/ajph.85.4.474", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068875448"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2105/ajph.85.4.484", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068875452"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3434570", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070323837"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5271/sjweh.1480", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072736890"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.aje.a116237", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077135678"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1082525658", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083030652", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2005-05", 
    "datePublishedReg": "2005-05-01", 
    "description": "OBJECTIVE: Reporting categorical relative risk estimates for a series of exposure levels versus a common reference category is a widespread practice. In meta-analysis, categorical regression estimates a dose-response trend from such results. This method requires the assignment of a single score to each exposure category. We examined how closely meta-analytical categorical regression approximates the results of analysis based on the individual-level continuous exposure.\nMETHODS: The analysis included five studies on tea intake and outcomes related to colorectal cancer. In addition, we derived categorical mean and median values from published distributions of tea consumption in similar populations to assign scores to the categories of tea intake when possible. We examined whether these derived mean and median values well approximate the individual-level results.\nRESULTS: In meta-analytical categorical regression, using the midrange scores approximated the individual-level continuous analyses reasonably well, if the value assigned to the uppermost, open-ended category was at least as high as the lower bound plus the width of the second-highest category. Categorical mean values derived from the published distributions of regular tea (in the US) and green tea (in Japan) well approximated the slope obtained from individual-level analysis.\nCONCLUSION: Publication of both the categorical and the continuous estimates of effect in primary studies, with their standard errors, can enhance the quality of meta-analysis, as well as providing intrinsically valuable information on dose-response.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10552-004-5025-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1100917", 
        "issn": [
          "0957-5243", 
          "1573-7225"
        ], 
        "name": "Cancer Causes & Control", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "16"
      }
    ], 
    "name": "Choice of exposure scores for categorical regression in meta-analysis: a case study of a common problem", 
    "pagination": "383-388", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "976e6e0d7dd0018152d293ca145b01cd07b72aeaefb7ebbb3fe2d2daab2c432a"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "15953980"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9100846"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10552-004-5025-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1031787925"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10552-004-5025-x", 
      "https://app.dimensions.ai/details/publication/pub.1031787925"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:30", 
    "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/v90/0000000373_0000000373/records_13093_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s10552-004-5025-x"
  }
]
 

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.10.1007/s10552-004-5025-x'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10552-004-5025-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10552-004-5025-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10552-004-5025-x'


 

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

253 TRIPLES      21 PREDICATES      72 URIs      38 LITERALS      26 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10552-004-5025-x schema:about N0142455c6ad348949dfa4912aa4dc5ef
2 N03e2b3c79f804b9cb224b3ea45b67065
3 N189669e5cb914bfc912ee51aa4c90fa0
4 N23bd310dff834376b43853aea53f3c4e
5 N2c3497167edf47bb8b6b6589f43b4302
6 N3a5b1959d14544f4bf5b9e46f5d0363a
7 N5636ed5cc92e447f888314929509bd2c
8 N74658bec5f344a06a72c5f995e81bd1c
9 N8745fdb06a4d4a6ab52baabb337546ea
10 N9a601228019b4905a2f430510fbaaa14
11 N9dc0aacf45224fc88a5c096882037675
12 Nb2665b6f211845d7a02b9242b88dd82c
13 Nb2af203ecd3844df934d7f9321465a38
14 Nb7ea0daebd4c4cfab6a300a681deb505
15 Nca91a74f1aea43058f3096b46ad0b570
16 Ne3af24d4cf7148bdab833a68c78cc0fe
17 Nfd31bf97ed9241b0a47ea9e6b4ede774
18 anzsrc-for:11
19 anzsrc-for:1117
20 schema:author Nd2152a45f9474cdaad1cc7a681a41d0d
21 schema:citation sg:pub.10.1054/bjoc.2001.2040
22 https://app.dimensions.ai/details/publication/pub.1082525658
23 https://app.dimensions.ai/details/publication/pub.1083030652
24 https://doi.org/10.1001/jama.289.5.579
25 https://doi.org/10.1002/ijc.10126
26 https://doi.org/10.1002/ijc.2910410510
27 https://doi.org/10.1002/sim.1595
28 https://doi.org/10.1016/0895-4356(91)90158-6
29 https://doi.org/10.1016/s0895-4356(96)00297-1
30 https://doi.org/10.1016/s1047-2797(02)00459-3
31 https://doi.org/10.1079/phn2001314
32 https://doi.org/10.1093/aje/154.6.495
33 https://doi.org/10.1093/aje/kwh142
34 https://doi.org/10.1093/humrep/17.9.2307
35 https://doi.org/10.1093/ije/31.1.59
36 https://doi.org/10.1093/oxfordjournals.aje.a010035
37 https://doi.org/10.1093/oxfordjournals.aje.a116237
38 https://doi.org/10.1097/00001648-199305000-00005
39 https://doi.org/10.1097/00001648-200105000-00005
40 https://doi.org/10.1111/j.0006-341x.2001.00671.x
41 https://doi.org/10.1136/jech.57.3.166
42 https://doi.org/10.1289/ehp.99107s6885
43 https://doi.org/10.2105/ajph.85.4.474
44 https://doi.org/10.2105/ajph.85.4.484
45 https://doi.org/10.2307/3434570
46 https://doi.org/10.5271/sjweh.1480
47 schema:datePublished 2005-05
48 schema:datePublishedReg 2005-05-01
49 schema:description OBJECTIVE: Reporting categorical relative risk estimates for a series of exposure levels versus a common reference category is a widespread practice. In meta-analysis, categorical regression estimates a dose-response trend from such results. This method requires the assignment of a single score to each exposure category. We examined how closely meta-analytical categorical regression approximates the results of analysis based on the individual-level continuous exposure. METHODS: The analysis included five studies on tea intake and outcomes related to colorectal cancer. In addition, we derived categorical mean and median values from published distributions of tea consumption in similar populations to assign scores to the categories of tea intake when possible. We examined whether these derived mean and median values well approximate the individual-level results. RESULTS: In meta-analytical categorical regression, using the midrange scores approximated the individual-level continuous analyses reasonably well, if the value assigned to the uppermost, open-ended category was at least as high as the lower bound plus the width of the second-highest category. Categorical mean values derived from the published distributions of regular tea (in the US) and green tea (in Japan) well approximated the slope obtained from individual-level analysis. CONCLUSION: Publication of both the categorical and the continuous estimates of effect in primary studies, with their standard errors, can enhance the quality of meta-analysis, as well as providing intrinsically valuable information on dose-response.
50 schema:genre research_article
51 schema:inLanguage en
52 schema:isAccessibleForFree false
53 schema:isPartOf Nd3725a81d540459793ec9be6c1250438
54 Necf0d6e0efdd461aa55ef86ec0a13166
55 sg:journal.1100917
56 schema:name Choice of exposure scores for categorical regression in meta-analysis: a case study of a common problem
57 schema:pagination 383-388
58 schema:productId N228935a352564888886fd7dc9e636f71
59 N5b727f9ff2c941efbba7abcd10e5499b
60 N6115e73c86244687865c96f7faaeca88
61 Nd1eb97dad67d4b499f5784ce30b7e9d5
62 Ne394ff1161ae4e3eb49c31b474f6e6e2
63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031787925
64 https://doi.org/10.1007/s10552-004-5025-x
65 schema:sdDatePublished 2019-04-11T14:30
66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
67 schema:sdPublisher N5d3b4131269c4fe0bd98f6d4cf8643f2
68 schema:url http://link.springer.com/10.1007/s10552-004-5025-x
69 sgo:license sg:explorer/license/
70 sgo:sdDataset articles
71 rdf:type schema:ScholarlyArticle
72 N0142455c6ad348949dfa4912aa4dc5ef schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
73 schema:name Colorectal Neoplasms
74 rdf:type schema:DefinedTerm
75 N03e2b3c79f804b9cb224b3ea45b67065 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
76 schema:name Regression Analysis
77 rdf:type schema:DefinedTerm
78 N1660c3d34b08410791c7c08b4371dddf rdf:first sg:person.0775617774.23
79 rdf:rest N1e338384d85f4f368b4c6ea8d1b01630
80 N189669e5cb914bfc912ee51aa4c90fa0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
81 schema:name Incidence
82 rdf:type schema:DefinedTerm
83 N1e338384d85f4f368b4c6ea8d1b01630 rdf:first sg:person.01033315763.32
84 rdf:rest N5fafa4511d0145d1a4d9155e76c425e1
85 N228935a352564888886fd7dc9e636f71 schema:name pubmed_id
86 schema:value 15953980
87 rdf:type schema:PropertyValue
88 N23bd310dff834376b43853aea53f3c4e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
89 schema:name Female
90 rdf:type schema:DefinedTerm
91 N2c3497167edf47bb8b6b6589f43b4302 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
92 schema:name Tea
93 rdf:type schema:DefinedTerm
94 N3a5b1959d14544f4bf5b9e46f5d0363a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
95 schema:name Confidence Intervals
96 rdf:type schema:DefinedTerm
97 N5636ed5cc92e447f888314929509bd2c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
98 schema:name Male
99 rdf:type schema:DefinedTerm
100 N5b727f9ff2c941efbba7abcd10e5499b schema:name nlm_unique_id
101 schema:value 9100846
102 rdf:type schema:PropertyValue
103 N5d3b4131269c4fe0bd98f6d4cf8643f2 schema:name Springer Nature - SN SciGraph project
104 rdf:type schema:Organization
105 N5fafa4511d0145d1a4d9155e76c425e1 rdf:first sg:person.0673476060.97
106 rdf:rest N7b6b4e09dfbb4907a6d2326e9e992d05
107 N6115e73c86244687865c96f7faaeca88 schema:name dimensions_id
108 schema:value pub.1031787925
109 rdf:type schema:PropertyValue
110 N74658bec5f344a06a72c5f995e81bd1c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Risk Assessment
112 rdf:type schema:DefinedTerm
113 N7b6b4e09dfbb4907a6d2326e9e992d05 rdf:first sg:person.01314504407.65
114 rdf:rest rdf:nil
115 N8745fdb06a4d4a6ab52baabb337546ea schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Sex Distribution
117 rdf:type schema:DefinedTerm
118 N9a601228019b4905a2f430510fbaaa14 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Humans
120 rdf:type schema:DefinedTerm
121 N9dc0aacf45224fc88a5c096882037675 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
122 schema:name United States
123 rdf:type schema:DefinedTerm
124 Nb2665b6f211845d7a02b9242b88dd82c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Poisson Distribution
126 rdf:type schema:DefinedTerm
127 Nb2af203ecd3844df934d7f9321465a38 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Time Factors
129 rdf:type schema:DefinedTerm
130 Nb7ea0daebd4c4cfab6a300a681deb505 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Primary Prevention
132 rdf:type schema:DefinedTerm
133 Nca91a74f1aea43058f3096b46ad0b570 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Odds Ratio
135 rdf:type schema:DefinedTerm
136 Nd1eb97dad67d4b499f5784ce30b7e9d5 schema:name doi
137 schema:value 10.1007/s10552-004-5025-x
138 rdf:type schema:PropertyValue
139 Nd2152a45f9474cdaad1cc7a681a41d0d rdf:first sg:person.01007456337.43
140 rdf:rest N1660c3d34b08410791c7c08b4371dddf
141 Nd3725a81d540459793ec9be6c1250438 schema:issueNumber 4
142 rdf:type schema:PublicationIssue
143 Ne394ff1161ae4e3eb49c31b474f6e6e2 schema:name readcube_id
144 schema:value 976e6e0d7dd0018152d293ca145b01cd07b72aeaefb7ebbb3fe2d2daab2c432a
145 rdf:type schema:PropertyValue
146 Ne3af24d4cf7148bdab833a68c78cc0fe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
147 schema:name Age Distribution
148 rdf:type schema:DefinedTerm
149 Necf0d6e0efdd461aa55ef86ec0a13166 schema:volumeNumber 16
150 rdf:type schema:PublicationVolume
151 Nfd31bf97ed9241b0a47ea9e6b4ede774 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Cohort Studies
153 rdf:type schema:DefinedTerm
154 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
155 schema:name Medical and Health Sciences
156 rdf:type schema:DefinedTerm
157 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
158 schema:name Public Health and Health Services
159 rdf:type schema:DefinedTerm
160 sg:journal.1100917 schema:issn 0957-5243
161 1573-7225
162 schema:name Cancer Causes & Control
163 rdf:type schema:Periodical
164 sg:person.01007456337.43 schema:affiliation https://www.grid.ac/institutes/grid.410711.2
165 schema:familyName Il’yasova
166 schema:givenName Dora
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007456337.43
168 rdf:type schema:Person
169 sg:person.01033315763.32 schema:affiliation https://www.grid.ac/institutes/grid.420086.8
170 schema:familyName Peters
171 schema:givenName Ulrike
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01033315763.32
173 rdf:type schema:Person
174 sg:person.01314504407.65 schema:affiliation https://www.grid.ac/institutes/grid.410711.2
175 schema:familyName Poole
176 schema:givenName Charles
177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01314504407.65
178 rdf:type schema:Person
179 sg:person.0673476060.97 schema:affiliation https://www.grid.ac/institutes/grid.25879.31
180 schema:familyName Berlin
181 schema:givenName Jesse A.
182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0673476060.97
183 rdf:type schema:Person
184 sg:person.0775617774.23 schema:affiliation https://www.grid.ac/institutes/grid.27860.3b
185 schema:familyName Hertz-Picciotto
186 schema:givenName Irva
187 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0775617774.23
188 rdf:type schema:Person
189 sg:pub.10.1054/bjoc.2001.2040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026630048
190 https://doi.org/10.1054/bjoc.2001.2040
191 rdf:type schema:CreativeWork
192 https://app.dimensions.ai/details/publication/pub.1082525658 schema:CreativeWork
193 https://app.dimensions.ai/details/publication/pub.1083030652 schema:CreativeWork
194 https://doi.org/10.1001/jama.289.5.579 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027136581
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1002/ijc.10126 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009686761
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1002/ijc.2910410510 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035465216
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1002/sim.1595 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025416390
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1016/0895-4356(91)90158-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003158896
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1016/s0895-4356(96)00297-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025852750
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1016/s1047-2797(02)00459-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052121883
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1079/phn2001314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017460353
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1093/aje/154.6.495 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011558881
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1093/aje/kwh142 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026635931
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1093/humrep/17.9.2307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018539626
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1093/ije/31.1.59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011259635
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1093/oxfordjournals.aje.a010035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000007454
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1093/oxfordjournals.aje.a116237 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077135678
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1097/00001648-199305000-00005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044526465
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1097/00001648-200105000-00005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043322471
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1111/j.0006-341x.2001.00671.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1002607343
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1136/jech.57.3.166 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052429031
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1289/ehp.99107s6885 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064747540
231 rdf:type schema:CreativeWork
232 https://doi.org/10.2105/ajph.85.4.474 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068875448
233 rdf:type schema:CreativeWork
234 https://doi.org/10.2105/ajph.85.4.484 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068875452
235 rdf:type schema:CreativeWork
236 https://doi.org/10.2307/3434570 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070323837
237 rdf:type schema:CreativeWork
238 https://doi.org/10.5271/sjweh.1480 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072736890
239 rdf:type schema:CreativeWork
240 https://www.grid.ac/institutes/grid.25879.31 schema:alternateName University of Pennsylvania
241 schema:name Center for Clinical Epidemiology and Biostatistics and Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, USA
242 rdf:type schema:Organization
243 https://www.grid.ac/institutes/grid.27860.3b schema:alternateName University of California, Davis
244 schema:name Department Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
245 Department of Epidemiology and Preventive Medicine, University of California, Davis, USA
246 rdf:type schema:Organization
247 https://www.grid.ac/institutes/grid.410711.2 schema:alternateName University of North Carolina System
248 schema:name Department Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
249 Medical Center Boulevard, Wake Forest University School of Medicine, 27157, Winston-Salem, NC
250 rdf:type schema:Organization
251 https://www.grid.ac/institutes/grid.420086.8 schema:alternateName National Institute of Arthritis and Musculoskeletal and Skin Diseases
252 schema:name Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Bethesda, Maryland, USA
253 rdf:type schema:Organization
 




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


...