Comparing two diagnostic tests when two tests are applied to same patients and test scores are given in categories View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-05

AUTHORS

Yoshiko Aoyama, Kenta Murotani, Takashi Yanagawa, Shuji Nagata

ABSTRACT

Suppose that a new diagnostic test is developed for making sharp distinction between disease A and B. To show its superiority to the standard test, the study design is introduced that the new and standard test are applied blindly to each patient in two groups, one group is definitely known being contracted with disease A and the other definitely known being contracted with disease B, where test scores are given in categories by multiple readers (raters). The design inevitably limits the number of patients used for the comparison and dependency could be introduced between the results of two tests. Application of existing statistical methods is not easy to be justified since they are based on asymptotic distributions of test statistics. We develop in this paper a method based on conditional logistic regressions that is applicable to small size of data and is useful to show the superiority of the new test to standard test by adjusting for the effect of readers in the study design. The method is applied to the data for comparing a new test and standard test for differentiating between epidermal cyst and ganglion. More... »

PAGES

44-55

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13571-012-0042-3

DOI

http://dx.doi.org/10.1007/s13571-012-0042-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018210417


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/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Kurume University Center of Biostatistics, Kurume City, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410781.b", 
          "name": [
            "Kurume University Center of Biostatistics, Kurume City, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Aoyama", 
        "givenName": "Yoshiko", 
        "id": "sg:person.01240174627.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240174627.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kurume University Center of Biostatistics, Kurume City, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410781.b", 
          "name": [
            "Kurume University Center of Biostatistics, Kurume City, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Murotani", 
        "givenName": "Kenta", 
        "id": "sg:person.01002652037.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01002652037.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kurume University Center of Biostatistics, Kurume City, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410781.b", 
          "name": [
            "Kurume University Center of Biostatistics, Kurume City, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yanagawa", 
        "givenName": "Takashi", 
        "id": "sg:person.013516437375.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013516437375.65"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The Department of Radiology Kurume University Faculty of Medicine, Kurume City, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410781.b", 
          "name": [
            "The Department of Radiology Kurume University Faculty of Medicine, Kurume City, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nagata", 
        "givenName": "Shuji", 
        "id": "sg:person.011705476215.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011705476215.09"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2012-05", 
    "datePublishedReg": "2012-05-01", 
    "description": "Suppose that a new diagnostic test is developed for making sharp distinction between disease A and B. To show its superiority to the standard test, the study design is introduced that the new and standard test are applied blindly to each patient in two groups, one group is definitely known being contracted with disease A and the other definitely known being contracted with disease B, where test scores are given in categories by multiple readers (raters). The design inevitably limits the number of patients used for the comparison and dependency could be introduced between the results of two tests. Application of existing statistical methods is not easy to be justified since they are based on asymptotic distributions of test statistics. We develop in this paper a method based on conditional logistic regressions that is applicable to small size of data and is useful to show the superiority of the new test to standard test by adjusting for the effect of readers in the study design. The method is applied to the data for comparing a new test and standard test for differentiating between epidermal cyst and ganglion.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s13571-012-0042-3", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1313561", 
        "issn": [
          "0976-8386", 
          "0976-8394"
        ], 
        "name": "Sankhya B", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "74"
      }
    ], 
    "keywords": [
      "statistical methods", 
      "asymptotic distribution", 
      "test statistic", 
      "superiority", 
      "statistics", 
      "new test", 
      "design", 
      "multiple readers", 
      "applications", 
      "standard tests", 
      "regression", 
      "distribution", 
      "categories", 
      "number", 
      "sharp distinction", 
      "dependency", 
      "data", 
      "results", 
      "readers", 
      "logistic regression", 
      "comparison", 
      "study design", 
      "test", 
      "small size", 
      "size", 
      "distinction", 
      "new diagnostic tests", 
      "diagnostic tests", 
      "conditional logistic regression", 
      "group", 
      "test scores", 
      "effect", 
      "scores", 
      "same patient", 
      "number of patients", 
      "disease A", 
      "method", 
      "disease B", 
      "epidermal cysts", 
      "patients", 
      "paper", 
      "cysts", 
      "ganglia", 
      "effects of readers"
    ], 
    "name": "Comparing two diagnostic tests when two tests are applied to same patients and test scores are given in categories", 
    "pagination": "44-55", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1018210417"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13571-012-0042-3"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13571-012-0042-3", 
      "https://app.dimensions.ai/details/publication/pub.1018210417"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-11-01T18:18", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/article/article_566.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s13571-012-0042-3"
  }
]
 

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/s13571-012-0042-3'

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/s13571-012-0042-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13571-012-0042-3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13571-012-0042-3'


 

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

125 TRIPLES      21 PREDICATES      70 URIs      62 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s13571-012-0042-3 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N81ca79c5c71f4ff0bb5f94284b8082d7
4 schema:datePublished 2012-05
5 schema:datePublishedReg 2012-05-01
6 schema:description Suppose that a new diagnostic test is developed for making sharp distinction between disease A and B. To show its superiority to the standard test, the study design is introduced that the new and standard test are applied blindly to each patient in two groups, one group is definitely known being contracted with disease A and the other definitely known being contracted with disease B, where test scores are given in categories by multiple readers (raters). The design inevitably limits the number of patients used for the comparison and dependency could be introduced between the results of two tests. Application of existing statistical methods is not easy to be justified since they are based on asymptotic distributions of test statistics. We develop in this paper a method based on conditional logistic regressions that is applicable to small size of data and is useful to show the superiority of the new test to standard test by adjusting for the effect of readers in the study design. The method is applied to the data for comparing a new test and standard test for differentiating between epidermal cyst and ganglion.
7 schema:genre article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N3d4aa5a58a894647b7e420cf7a09f6e5
11 Nc63df555512c457283375abd0c140c4e
12 sg:journal.1313561
13 schema:keywords applications
14 asymptotic distribution
15 categories
16 comparison
17 conditional logistic regression
18 cysts
19 data
20 dependency
21 design
22 diagnostic tests
23 disease A
24 disease B
25 distinction
26 distribution
27 effect
28 effects of readers
29 epidermal cysts
30 ganglia
31 group
32 logistic regression
33 method
34 multiple readers
35 new diagnostic tests
36 new test
37 number
38 number of patients
39 paper
40 patients
41 readers
42 regression
43 results
44 same patient
45 scores
46 sharp distinction
47 size
48 small size
49 standard tests
50 statistical methods
51 statistics
52 study design
53 superiority
54 test
55 test scores
56 test statistic
57 schema:name Comparing two diagnostic tests when two tests are applied to same patients and test scores are given in categories
58 schema:pagination 44-55
59 schema:productId N39dc421d5db5417793f90651b1a33ce4
60 N5822980d425840509bcabc03212f969d
61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018210417
62 https://doi.org/10.1007/s13571-012-0042-3
63 schema:sdDatePublished 2021-11-01T18:18
64 schema:sdLicense https://scigraph.springernature.com/explorer/license/
65 schema:sdPublisher N4d229d22920e4c13895f41e82b054ae7
66 schema:url https://doi.org/10.1007/s13571-012-0042-3
67 sgo:license sg:explorer/license/
68 sgo:sdDataset articles
69 rdf:type schema:ScholarlyArticle
70 N39dc421d5db5417793f90651b1a33ce4 schema:name dimensions_id
71 schema:value pub.1018210417
72 rdf:type schema:PropertyValue
73 N3d4aa5a58a894647b7e420cf7a09f6e5 schema:issueNumber 1
74 rdf:type schema:PublicationIssue
75 N46fee1930321443db04949e510cabc8c rdf:first sg:person.01002652037.58
76 rdf:rest Nf6a9d0a20f6a4f059218523d6150a81a
77 N4d229d22920e4c13895f41e82b054ae7 schema:name Springer Nature - SN SciGraph project
78 rdf:type schema:Organization
79 N5822980d425840509bcabc03212f969d schema:name doi
80 schema:value 10.1007/s13571-012-0042-3
81 rdf:type schema:PropertyValue
82 N81ca79c5c71f4ff0bb5f94284b8082d7 rdf:first sg:person.01240174627.42
83 rdf:rest N46fee1930321443db04949e510cabc8c
84 Nc63df555512c457283375abd0c140c4e schema:volumeNumber 74
85 rdf:type schema:PublicationVolume
86 Nee50637336d048be9e7ec69a827d7e4e rdf:first sg:person.011705476215.09
87 rdf:rest rdf:nil
88 Nf6a9d0a20f6a4f059218523d6150a81a rdf:first sg:person.013516437375.65
89 rdf:rest Nee50637336d048be9e7ec69a827d7e4e
90 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
91 schema:name Mathematical Sciences
92 rdf:type schema:DefinedTerm
93 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
94 schema:name Statistics
95 rdf:type schema:DefinedTerm
96 sg:journal.1313561 schema:issn 0976-8386
97 0976-8394
98 schema:name Sankhya B
99 schema:publisher Springer Nature
100 rdf:type schema:Periodical
101 sg:person.01002652037.58 schema:affiliation grid-institutes:grid.410781.b
102 schema:familyName Murotani
103 schema:givenName Kenta
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01002652037.58
105 rdf:type schema:Person
106 sg:person.011705476215.09 schema:affiliation grid-institutes:grid.410781.b
107 schema:familyName Nagata
108 schema:givenName Shuji
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011705476215.09
110 rdf:type schema:Person
111 sg:person.01240174627.42 schema:affiliation grid-institutes:grid.410781.b
112 schema:familyName Aoyama
113 schema:givenName Yoshiko
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240174627.42
115 rdf:type schema:Person
116 sg:person.013516437375.65 schema:affiliation grid-institutes:grid.410781.b
117 schema:familyName Yanagawa
118 schema:givenName Takashi
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013516437375.65
120 rdf:type schema:Person
121 grid-institutes:grid.410781.b schema:alternateName Kurume University Center of Biostatistics, Kurume City, Japan
122 The Department of Radiology Kurume University Faculty of Medicine, Kurume City, Japan
123 schema:name Kurume University Center of Biostatistics, Kurume City, Japan
124 The Department of Radiology Kurume University Faculty of Medicine, Kurume City, Japan
125 rdf:type schema:Organization
 




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


...