Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-08-31

AUTHORS

Jacques-Emmanuel Galimard, Sylvie Chevret, Emmanuel Curis, Matthieu Resche-Rigon

ABSTRACT

BackgroundMultiple imputation by chained equations (MICE) requires specifying a suitable conditional imputation model for each incomplete variable and then iteratively imputes the missing values. In the presence of missing not at random (MNAR) outcomes, valid statistical inference often requires joint models for missing observations and their indicators of missingness. In this study, we derived an imputation model for missing binary data with MNAR mechanism from Heckman’s model using a one-step maximum likelihood estimator. We applied this approach to improve a previously developed approach for MNAR continuous outcomes using Heckman’s model and a two-step estimator. These models allow us to use a MICE process and can thus also handle missing at random (MAR) predictors in the same MICE process.MethodsWe simulated 1000 datasets of 500 cases. We generated the following missing data mechanisms on 30% of the outcomes: MAR mechanism, weak MNAR mechanism, and strong MNAR mechanism. We then resimulated the first three cases and added an additional 30% of MAR data on a predictor, resulting in 50% of complete cases. We evaluated and compared the performance of the developed approach to that of a complete case approach and classical Heckman’s model estimates.ResultsWith MNAR outcomes, only methods using Heckman’s model were unbiased, and with a MAR predictor, the developed imputation approach outperformed all the other approaches.ConclusionsIn the presence of MAR predictors, we proposed a simple approach to address MNAR binary or continuous outcomes under a Heckman assumption in a MICE procedure. More... »

PAGES

90

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12874-018-0547-1

DOI

http://dx.doi.org/10.1186/s12874-018-0547-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106471162

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30170561


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/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Accuracy", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Interpretation, Statistical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Epidemiology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Likelihood Functions", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Theoretical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Monte Carlo Method", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Outcome Assessment, Health Care", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Paris Diderot University \u2013 Paris 7, Sorbonne Paris Cit\u00e9, F-75010, Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.508487.6", 
          "name": [
            "INSERM U1153, Epidemiology and Biostatistics Sorbonne Paris Cit\u00e9 Research Center (CRESS), ECSTRA team, Service de Biostatistique et Information M\u00e9dicale, H\u00f4pital Saint-Louis, AP-HP, 1 avenue Claude Vellefaux, F-75010, Paris, France", 
            "Paris Diderot University \u2013 Paris 7, Sorbonne Paris Cit\u00e9, F-75010, Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Galimard", 
        "givenName": "Jacques-Emmanuel", 
        "id": "sg:person.014653336161.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014653336161.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Service de Biostatistique et Information M\u00e9dicale, H\u00f4pital Saint-Louis, F-75010, Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.413328.f", 
          "name": [
            "INSERM U1153, Epidemiology and Biostatistics Sorbonne Paris Cit\u00e9 Research Center (CRESS), ECSTRA team, Service de Biostatistique et Information M\u00e9dicale, H\u00f4pital Saint-Louis, AP-HP, 1 avenue Claude Vellefaux, F-75010, Paris, France", 
            "Paris Diderot University \u2013 Paris 7, Sorbonne Paris Cit\u00e9, F-75010, Paris, France", 
            "Service de Biostatistique et Information M\u00e9dicale, H\u00f4pital Saint-Louis, F-75010, Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chevret", 
        "givenName": "Sylvie", 
        "id": "sg:person.0625147223.89", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0625147223.89"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratoire de biomath\u00e9matiques \u2013 plateau iB2, facult\u00e9 de pharmacie, Universit\u00e9 Paris Descartes, Sorbonne Paris Cit\u00e9, F-75006, Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.508487.6", 
          "name": [
            "INSERM UMR-S 1144, \u00c9quipe 1, Universit\u00e9 Paris Descartes, Universit\u00e9 Paris Diderot, Sorbonne Paris Cit\u00e9, F-75013, Paris, France", 
            "Laboratoire de biomath\u00e9matiques \u2013 plateau iB2, facult\u00e9 de pharmacie, Universit\u00e9 Paris Descartes, Sorbonne Paris Cit\u00e9, F-75006, Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Curis", 
        "givenName": "Emmanuel", 
        "id": "sg:person.0632566044.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0632566044.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Service de Biostatistique et Information M\u00e9dicale, H\u00f4pital Saint-Louis, F-75010, Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.413328.f", 
          "name": [
            "INSERM U1153, Epidemiology and Biostatistics Sorbonne Paris Cit\u00e9 Research Center (CRESS), ECSTRA team, Service de Biostatistique et Information M\u00e9dicale, H\u00f4pital Saint-Louis, AP-HP, 1 avenue Claude Vellefaux, F-75010, Paris, France", 
            "Paris Diderot University \u2013 Paris 7, Sorbonne Paris Cit\u00e9, F-75010, Paris, France", 
            "Service de Biostatistique et Information M\u00e9dicale, H\u00f4pital Saint-Louis, F-75010, Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Resche-Rigon", 
        "givenName": "Matthieu", 
        "id": "sg:person.01036155166.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01036155166.50"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10940-007-9024-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005731594", 
          "https://doi.org/10.1007/s10940-007-9024-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1756-0500-5-330", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001995043", 
          "https://doi.org/10.1186/1756-0500-5-330"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2288-14-28", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029580860", 
          "https://doi.org/10.1186/1471-2288-14-28"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-4976-4_10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026496105", 
          "https://doi.org/10.1007/978-1-4612-4976-4_10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11123-009-0159-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008144378", 
          "https://doi.org/10.1007/s11123-009-0159-1"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-08-31", 
    "datePublishedReg": "2018-08-31", 
    "description": "BackgroundMultiple imputation by chained equations (MICE) requires specifying a suitable conditional imputation model for each incomplete variable and then iteratively imputes the missing values. In the presence of missing not at random (MNAR) outcomes, valid statistical inference often requires joint models for missing observations and their indicators of missingness. In this study, we derived an imputation model for missing binary data with MNAR mechanism from Heckman\u2019s model using a one-step maximum likelihood estimator. We applied this approach to improve a previously developed approach for MNAR continuous outcomes using Heckman\u2019s model and a two-step estimator. These models allow us to use a MICE process and can thus also handle missing at random (MAR) predictors in the same MICE process.MethodsWe simulated 1000 datasets of 500 cases. We generated the following missing data mechanisms on 30% of the outcomes: MAR mechanism, weak MNAR mechanism, and strong MNAR mechanism. We then resimulated the first three cases and added an additional 30% of MAR data on a predictor, resulting in 50% of complete cases. We evaluated and compared the performance of the developed approach to that of a complete case approach and classical Heckman\u2019s model estimates.ResultsWith MNAR outcomes, only methods using Heckman\u2019s model were unbiased, and with a MAR predictor, the developed imputation approach outperformed all the other approaches.ConclusionsIn the presence of MAR predictors, we proposed a simple approach to address MNAR binary or continuous outcomes under a Heckman assumption in a MICE procedure.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12874-018-0547-1", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1024940", 
        "issn": [
          "1471-2288"
        ], 
        "name": "BMC Medical Research Methodology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "18"
      }
    ], 
    "keywords": [
      "MNAR mechanism", 
      "imputation model", 
      "indicators of missingness", 
      "valid statistical inference", 
      "one-step maximum likelihood estimator", 
      "maximum likelihood estimator", 
      "two-step estimator", 
      "statistical inference", 
      "MAR mechanism", 
      "incomplete variables", 
      "data mechanism", 
      "likelihood estimator", 
      "random outcomes", 
      "random predictor", 
      "imputation approach", 
      "estimator", 
      "complete case approach", 
      "binary data", 
      "joint model", 
      "Mars data", 
      "continuous outcomes", 
      "simple approach", 
      "complete cases", 
      "equations", 
      "MNAR", 
      "model", 
      "missingness", 
      "Heckman model", 
      "approach", 
      "inference", 
      "mice process", 
      "imputation", 
      "assumption", 
      "only method", 
      "cases", 
      "variables", 
      "performance", 
      "data", 
      "process", 
      "observations", 
      "dataset", 
      "procedure", 
      "values", 
      "presence", 
      "case approach", 
      "mechanism", 
      "indicators", 
      "study", 
      "predictors", 
      "ConclusionsIn", 
      "outcomes", 
      "method", 
      "MethodsWe"
    ], 
    "name": "Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors", 
    "pagination": "90", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1106471162"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12874-018-0547-1"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30170561"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12874-018-0547-1", 
      "https://app.dimensions.ai/details/publication/pub.1106471162"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:37", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_782.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12874-018-0547-1"
  }
]
 

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/s12874-018-0547-1'

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/s12874-018-0547-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12874-018-0547-1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12874-018-0547-1'


 

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

199 TRIPLES      21 PREDICATES      92 URIs      79 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12874-018-0547-1 schema:about N40ed0a9ff674436e987f667f3302be3d
2 N699659c3e1144a31bf1a632cc2d6ab53
3 N83bbf5c821374bd08bee207734e52c63
4 N95cf221e3c6a47238ec9d1401602f86a
5 Na80c9c7b0017474ca072392e74437d48
6 Ncacc9a441be3427c8217a6e2ecd3f1f8
7 Nd43091ac50ad45f6a10665d612c32b21
8 Ndc1b3d48c4f2450788482b0da309e2e0
9 Nf25bfa86da134eb8bc6caad0a54fa87c
10 anzsrc-for:11
11 anzsrc-for:1117
12 schema:author N7af8ccfde8ac474d8ce3c99008268c24
13 schema:citation sg:pub.10.1007/978-1-4612-4976-4_10
14 sg:pub.10.1007/s10940-007-9024-4
15 sg:pub.10.1007/s11123-009-0159-1
16 sg:pub.10.1186/1471-2288-14-28
17 sg:pub.10.1186/1756-0500-5-330
18 schema:datePublished 2018-08-31
19 schema:datePublishedReg 2018-08-31
20 schema:description BackgroundMultiple imputation by chained equations (MICE) requires specifying a suitable conditional imputation model for each incomplete variable and then iteratively imputes the missing values. In the presence of missing not at random (MNAR) outcomes, valid statistical inference often requires joint models for missing observations and their indicators of missingness. In this study, we derived an imputation model for missing binary data with MNAR mechanism from Heckman’s model using a one-step maximum likelihood estimator. We applied this approach to improve a previously developed approach for MNAR continuous outcomes using Heckman’s model and a two-step estimator. These models allow us to use a MICE process and can thus also handle missing at random (MAR) predictors in the same MICE process.MethodsWe simulated 1000 datasets of 500 cases. We generated the following missing data mechanisms on 30% of the outcomes: MAR mechanism, weak MNAR mechanism, and strong MNAR mechanism. We then resimulated the first three cases and added an additional 30% of MAR data on a predictor, resulting in 50% of complete cases. We evaluated and compared the performance of the developed approach to that of a complete case approach and classical Heckman’s model estimates.ResultsWith MNAR outcomes, only methods using Heckman’s model were unbiased, and with a MAR predictor, the developed imputation approach outperformed all the other approaches.ConclusionsIn the presence of MAR predictors, we proposed a simple approach to address MNAR binary or continuous outcomes under a Heckman assumption in a MICE procedure.
21 schema:genre article
22 schema:isAccessibleForFree true
23 schema:isPartOf N6f1d9b3a5f3a4930b5d29e84738466fb
24 Nfca3646f6e934229b891db7a62958089
25 sg:journal.1024940
26 schema:keywords ConclusionsIn
27 Heckman model
28 MAR mechanism
29 MNAR
30 MNAR mechanism
31 Mars data
32 MethodsWe
33 approach
34 assumption
35 binary data
36 case approach
37 cases
38 complete case approach
39 complete cases
40 continuous outcomes
41 data
42 data mechanism
43 dataset
44 equations
45 estimator
46 imputation
47 imputation approach
48 imputation model
49 incomplete variables
50 indicators
51 indicators of missingness
52 inference
53 joint model
54 likelihood estimator
55 maximum likelihood estimator
56 mechanism
57 method
58 mice process
59 missingness
60 model
61 observations
62 one-step maximum likelihood estimator
63 only method
64 outcomes
65 performance
66 predictors
67 presence
68 procedure
69 process
70 random outcomes
71 random predictor
72 simple approach
73 statistical inference
74 study
75 two-step estimator
76 valid statistical inference
77 values
78 variables
79 schema:name Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors
80 schema:pagination 90
81 schema:productId N086cdf4bbdb84b928fb3964ea52589f7
82 N6b2b36182e9441dfa79360ee5ef89d86
83 Nf21c28dd3c9c48e198f7a59307a6ceb7
84 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106471162
85 https://doi.org/10.1186/s12874-018-0547-1
86 schema:sdDatePublished 2022-12-01T06:37
87 schema:sdLicense https://scigraph.springernature.com/explorer/license/
88 schema:sdPublisher N71fd4688e46147bbb974fedf81158e25
89 schema:url https://doi.org/10.1186/s12874-018-0547-1
90 sgo:license sg:explorer/license/
91 sgo:sdDataset articles
92 rdf:type schema:ScholarlyArticle
93 N086cdf4bbdb84b928fb3964ea52589f7 schema:name dimensions_id
94 schema:value pub.1106471162
95 rdf:type schema:PropertyValue
96 N40ed0a9ff674436e987f667f3302be3d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Algorithms
98 rdf:type schema:DefinedTerm
99 N54eef42179414874afdb15a0508bd00e rdf:first sg:person.0625147223.89
100 rdf:rest N65834c34e5d64b9386794c58f6331933
101 N65834c34e5d64b9386794c58f6331933 rdf:first sg:person.0632566044.44
102 rdf:rest Nbf8b1ffaf8584ec288abf57f785fc0d1
103 N699659c3e1144a31bf1a632cc2d6ab53 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Likelihood Functions
105 rdf:type schema:DefinedTerm
106 N6b2b36182e9441dfa79360ee5ef89d86 schema:name pubmed_id
107 schema:value 30170561
108 rdf:type schema:PropertyValue
109 N6f1d9b3a5f3a4930b5d29e84738466fb schema:volumeNumber 18
110 rdf:type schema:PublicationVolume
111 N71fd4688e46147bbb974fedf81158e25 schema:name Springer Nature - SN SciGraph project
112 rdf:type schema:Organization
113 N7af8ccfde8ac474d8ce3c99008268c24 rdf:first sg:person.014653336161.39
114 rdf:rest N54eef42179414874afdb15a0508bd00e
115 N83bbf5c821374bd08bee207734e52c63 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Data Accuracy
117 rdf:type schema:DefinedTerm
118 N95cf221e3c6a47238ec9d1401602f86a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Data Interpretation, Statistical
120 rdf:type schema:DefinedTerm
121 Na80c9c7b0017474ca072392e74437d48 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
122 schema:name Models, Theoretical
123 rdf:type schema:DefinedTerm
124 Nbf8b1ffaf8584ec288abf57f785fc0d1 rdf:first sg:person.01036155166.50
125 rdf:rest rdf:nil
126 Ncacc9a441be3427c8217a6e2ecd3f1f8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name Epidemiology
128 rdf:type schema:DefinedTerm
129 Nd43091ac50ad45f6a10665d612c32b21 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Monte Carlo Method
131 rdf:type schema:DefinedTerm
132 Ndc1b3d48c4f2450788482b0da309e2e0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
133 schema:name Humans
134 rdf:type schema:DefinedTerm
135 Nf21c28dd3c9c48e198f7a59307a6ceb7 schema:name doi
136 schema:value 10.1186/s12874-018-0547-1
137 rdf:type schema:PropertyValue
138 Nf25bfa86da134eb8bc6caad0a54fa87c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
139 schema:name Outcome Assessment, Health Care
140 rdf:type schema:DefinedTerm
141 Nfca3646f6e934229b891db7a62958089 schema:issueNumber 1
142 rdf:type schema:PublicationIssue
143 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
144 schema:name Medical and Health Sciences
145 rdf:type schema:DefinedTerm
146 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
147 schema:name Public Health and Health Services
148 rdf:type schema:DefinedTerm
149 sg:journal.1024940 schema:issn 1471-2288
150 schema:name BMC Medical Research Methodology
151 schema:publisher Springer Nature
152 rdf:type schema:Periodical
153 sg:person.01036155166.50 schema:affiliation grid-institutes:grid.413328.f
154 schema:familyName Resche-Rigon
155 schema:givenName Matthieu
156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01036155166.50
157 rdf:type schema:Person
158 sg:person.014653336161.39 schema:affiliation grid-institutes:grid.508487.6
159 schema:familyName Galimard
160 schema:givenName Jacques-Emmanuel
161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014653336161.39
162 rdf:type schema:Person
163 sg:person.0625147223.89 schema:affiliation grid-institutes:grid.413328.f
164 schema:familyName Chevret
165 schema:givenName Sylvie
166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0625147223.89
167 rdf:type schema:Person
168 sg:person.0632566044.44 schema:affiliation grid-institutes:grid.508487.6
169 schema:familyName Curis
170 schema:givenName Emmanuel
171 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0632566044.44
172 rdf:type schema:Person
173 sg:pub.10.1007/978-1-4612-4976-4_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026496105
174 https://doi.org/10.1007/978-1-4612-4976-4_10
175 rdf:type schema:CreativeWork
176 sg:pub.10.1007/s10940-007-9024-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005731594
177 https://doi.org/10.1007/s10940-007-9024-4
178 rdf:type schema:CreativeWork
179 sg:pub.10.1007/s11123-009-0159-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008144378
180 https://doi.org/10.1007/s11123-009-0159-1
181 rdf:type schema:CreativeWork
182 sg:pub.10.1186/1471-2288-14-28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029580860
183 https://doi.org/10.1186/1471-2288-14-28
184 rdf:type schema:CreativeWork
185 sg:pub.10.1186/1756-0500-5-330 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001995043
186 https://doi.org/10.1186/1756-0500-5-330
187 rdf:type schema:CreativeWork
188 grid-institutes:grid.413328.f schema:alternateName Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, F-75010, Paris, France
189 schema:name INSERM U1153, Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), ECSTRA team, Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, 1 avenue Claude Vellefaux, F-75010, Paris, France
190 Paris Diderot University – Paris 7, Sorbonne Paris Cité, F-75010, Paris, France
191 Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, F-75010, Paris, France
192 rdf:type schema:Organization
193 grid-institutes:grid.508487.6 schema:alternateName Laboratoire de biomathématiques – plateau iB2, faculté de pharmacie, Université Paris Descartes, Sorbonne Paris Cité, F-75006, Paris, France
194 Paris Diderot University – Paris 7, Sorbonne Paris Cité, F-75010, Paris, France
195 schema:name INSERM U1153, Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), ECSTRA team, Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, 1 avenue Claude Vellefaux, F-75010, Paris, France
196 INSERM UMR-S 1144, Équipe 1, Université Paris Descartes, Université Paris Diderot, Sorbonne Paris Cité, F-75013, Paris, France
197 Laboratoire de biomathématiques – plateau iB2, faculté de pharmacie, Université Paris Descartes, Sorbonne Paris Cité, F-75006, Paris, France
198 Paris Diderot University – Paris 7, Sorbonne Paris Cité, F-75010, Paris, France
199 rdf:type schema:Organization
 




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


...