An algorithm for estimating survival under a copula-based dependent truncation model View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-03-10

AUTHORS

T. Emura, K. Murotani

ABSTRACT

Traditional analysis with truncated survival data has been developed under the assumption that the lifetime variable of interest is statistically independent of the truncation variable. However, empirical evidence has shown that the truncation variable may depend on the lifetime of interest in many real-world examples. The lack of independence can lead to seriously biased analysis. In this article, we revisit an existing estimation procedure for survival under a copula-based dependent truncation model. Here, the same estimating equation is adopted but a different algorithm to solve the equation is proposed. We compare the new algorithm with the existing one and discuss its theoretical and practical usefulness. Real data examples are analyzed for illustration. We implemented the proposed algorithm in an R “depend.truncation” package, available from CRAN. More... »

PAGES

734-751

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11749-015-0432-8

DOI

http://dx.doi.org/10.1007/s11749-015-0432-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025802603


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": "Graduate Institute of Statistics, National Central University, Jhongli, Taiwan", 
          "id": "http://www.grid.ac/institutes/grid.37589.30", 
          "name": [
            "Graduate Institute of Statistics, National Central University, Jhongli, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Emura", 
        "givenName": "T.", 
        "id": "sg:person.013174273301.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013174273301.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Advanced Medicine and Clinical Research, Nagoya University Hospital, Nagoya, Japan", 
          "id": "http://www.grid.ac/institutes/grid.437848.4", 
          "name": [
            "Center for Advanced Medicine and Clinical Research, Nagoya University Hospital, Nagoya, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Murotani", 
        "givenName": "K.", 
        "id": "sg:person.01002652037.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01002652037.58"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11749-013-0339-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037240938", 
          "https://doi.org/10.1007/s11749-013-0339-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00362-010-0321-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032259559", 
          "https://doi.org/10.1007/s00362-010-0321-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10985-012-9217-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012911811", 
          "https://doi.org/10.1007/s10985-012-9217-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/b97377", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013400699", 
          "https://doi.org/10.1007/b97377"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015-03-10", 
    "datePublishedReg": "2015-03-10", 
    "description": "Traditional analysis with truncated survival data has been developed under the assumption that the lifetime variable of interest is statistically independent of the truncation variable. However, empirical evidence has shown that the truncation variable may depend on the lifetime of interest in many real-world examples. The lack of independence can lead to seriously biased analysis. In this article, we revisit an existing estimation procedure for survival under a copula-based dependent truncation model. Here, the same estimating equation is adopted but a different algorithm to solve the equation is proposed. We compare the new algorithm with the existing one and discuss its theoretical and practical usefulness. Real data examples are analyzed for illustration. We implemented the proposed algorithm in an R \u201cdepend.truncation\u201d package, available from CRAN.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11749-015-0432-8", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1043392", 
        "issn": [
          "1133-0686", 
          "1863-8260"
        ], 
        "name": "TEST", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "24"
      }
    ], 
    "keywords": [
      "real-world examples", 
      "different algorithms", 
      "new algorithm", 
      "algorithm", 
      "real data example", 
      "practical usefulness", 
      "data examples", 
      "CRAN", 
      "truncation variable", 
      "estimation procedure", 
      "example", 
      "biased analysis", 
      "package", 
      "model", 
      "interest", 
      "traditional analysis", 
      "empirical evidence", 
      "truncation model", 
      "data", 
      "usefulness", 
      "lifetime", 
      "lack of independence", 
      "illustration", 
      "assumption", 
      "lifetime variable", 
      "analysis", 
      "variables", 
      "independence", 
      "lack", 
      "article", 
      "procedure", 
      "depend", 
      "truncation", 
      "evidence", 
      "equations", 
      "survival data", 
      "survival", 
      "lifetime of interest", 
      "copula-based dependent truncation model", 
      "dependent truncation model"
    ], 
    "name": "An algorithm for estimating survival under a copula-based dependent truncation model", 
    "pagination": "734-751", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1025802603"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11749-015-0432-8"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11749-015-0432-8", 
      "https://app.dimensions.ai/details/publication/pub.1025802603"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-11-01T18:24", 
    "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_658.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11749-015-0432-8"
  }
]
 

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/s11749-015-0432-8'

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/s11749-015-0432-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11749-015-0432-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11749-015-0432-8'


 

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

124 TRIPLES      22 PREDICATES      68 URIs      56 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11749-015-0432-8 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Nf2e9a0a4cdd443f98bdf0f56b23811b8
4 schema:citation sg:pub.10.1007/b97377
5 sg:pub.10.1007/s00362-010-0321-x
6 sg:pub.10.1007/s10985-012-9217-5
7 sg:pub.10.1007/s11749-013-0339-1
8 schema:datePublished 2015-03-10
9 schema:datePublishedReg 2015-03-10
10 schema:description Traditional analysis with truncated survival data has been developed under the assumption that the lifetime variable of interest is statistically independent of the truncation variable. However, empirical evidence has shown that the truncation variable may depend on the lifetime of interest in many real-world examples. The lack of independence can lead to seriously biased analysis. In this article, we revisit an existing estimation procedure for survival under a copula-based dependent truncation model. Here, the same estimating equation is adopted but a different algorithm to solve the equation is proposed. We compare the new algorithm with the existing one and discuss its theoretical and practical usefulness. Real data examples are analyzed for illustration. We implemented the proposed algorithm in an R “depend.truncation” package, available from CRAN.
11 schema:genre article
12 schema:inLanguage en
13 schema:isAccessibleForFree false
14 schema:isPartOf N78f4877a9a064954aee12ce7e79400be
15 Nc3f753fd8ed94d4782ee445366a747ee
16 sg:journal.1043392
17 schema:keywords CRAN
18 algorithm
19 analysis
20 article
21 assumption
22 biased analysis
23 copula-based dependent truncation model
24 data
25 data examples
26 depend
27 dependent truncation model
28 different algorithms
29 empirical evidence
30 equations
31 estimation procedure
32 evidence
33 example
34 illustration
35 independence
36 interest
37 lack
38 lack of independence
39 lifetime
40 lifetime of interest
41 lifetime variable
42 model
43 new algorithm
44 package
45 practical usefulness
46 procedure
47 real data example
48 real-world examples
49 survival
50 survival data
51 traditional analysis
52 truncation
53 truncation model
54 truncation variable
55 usefulness
56 variables
57 schema:name An algorithm for estimating survival under a copula-based dependent truncation model
58 schema:pagination 734-751
59 schema:productId N28c7bdcfa96a4686820061336b724629
60 N423d39654a514cec89cdd3ff9b89b7ad
61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025802603
62 https://doi.org/10.1007/s11749-015-0432-8
63 schema:sdDatePublished 2021-11-01T18:24
64 schema:sdLicense https://scigraph.springernature.com/explorer/license/
65 schema:sdPublisher N1af1a5ed22574ca1b971cc154f967482
66 schema:url https://doi.org/10.1007/s11749-015-0432-8
67 sgo:license sg:explorer/license/
68 sgo:sdDataset articles
69 rdf:type schema:ScholarlyArticle
70 N1af1a5ed22574ca1b971cc154f967482 schema:name Springer Nature - SN SciGraph project
71 rdf:type schema:Organization
72 N28c7bdcfa96a4686820061336b724629 schema:name doi
73 schema:value 10.1007/s11749-015-0432-8
74 rdf:type schema:PropertyValue
75 N423d39654a514cec89cdd3ff9b89b7ad schema:name dimensions_id
76 schema:value pub.1025802603
77 rdf:type schema:PropertyValue
78 N481dea37c34b43409253e8590c67a1e3 rdf:first sg:person.01002652037.58
79 rdf:rest rdf:nil
80 N78f4877a9a064954aee12ce7e79400be schema:issueNumber 4
81 rdf:type schema:PublicationIssue
82 Nc3f753fd8ed94d4782ee445366a747ee schema:volumeNumber 24
83 rdf:type schema:PublicationVolume
84 Nf2e9a0a4cdd443f98bdf0f56b23811b8 rdf:first sg:person.013174273301.33
85 rdf:rest N481dea37c34b43409253e8590c67a1e3
86 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
87 schema:name Mathematical Sciences
88 rdf:type schema:DefinedTerm
89 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
90 schema:name Statistics
91 rdf:type schema:DefinedTerm
92 sg:journal.1043392 schema:issn 1133-0686
93 1863-8260
94 schema:name TEST
95 schema:publisher Springer Nature
96 rdf:type schema:Periodical
97 sg:person.01002652037.58 schema:affiliation grid-institutes:grid.437848.4
98 schema:familyName Murotani
99 schema:givenName K.
100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01002652037.58
101 rdf:type schema:Person
102 sg:person.013174273301.33 schema:affiliation grid-institutes:grid.37589.30
103 schema:familyName Emura
104 schema:givenName T.
105 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013174273301.33
106 rdf:type schema:Person
107 sg:pub.10.1007/b97377 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013400699
108 https://doi.org/10.1007/b97377
109 rdf:type schema:CreativeWork
110 sg:pub.10.1007/s00362-010-0321-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1032259559
111 https://doi.org/10.1007/s00362-010-0321-x
112 rdf:type schema:CreativeWork
113 sg:pub.10.1007/s10985-012-9217-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012911811
114 https://doi.org/10.1007/s10985-012-9217-5
115 rdf:type schema:CreativeWork
116 sg:pub.10.1007/s11749-013-0339-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037240938
117 https://doi.org/10.1007/s11749-013-0339-1
118 rdf:type schema:CreativeWork
119 grid-institutes:grid.37589.30 schema:alternateName Graduate Institute of Statistics, National Central University, Jhongli, Taiwan
120 schema:name Graduate Institute of Statistics, National Central University, Jhongli, Taiwan
121 rdf:type schema:Organization
122 grid-institutes:grid.437848.4 schema:alternateName Center for Advanced Medicine and Clinical Research, Nagoya University Hospital, Nagoya, Japan
123 schema:name Center for Advanced Medicine and Clinical Research, Nagoya University Hospital, Nagoya, Japan
124 rdf:type schema:Organization
 




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


...