Application of trajectories from growth curve in identification of longitudinal biomarker for the multivariate survival data
In clinical studies, the researchers measure the patients' response longitudinally. In recent studies, Mixed models are used to determine effects in the individual level. In the other hand, Henderson et al. [3,4] developed a joint likelihood function which combines likelihood functions of longi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ko, Feng-shou [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
|---|
| Rechteinformationen: |
Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of applied statistics - Abingdon [u.a.] : Taylor & Francis, 1984, 44(2017), 3, Seite 416-11 |
|---|---|
| Übergeordnetes Werk: |
volume:44 ; year:2017 ; number:3 ; pages:416-11 |
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| DOI / URN: |
10.1080/02664763.2016.1174196 |
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10.1080/02664763.2016.1174196 doi PQ20170301 (DE-627)OLC1990302211 (DE-599)GBVOLC1990302211 (PRQ)c1593-c57679f930f26a486e4cc03f3c21d28355d3931952509d3c1b7c486983fab1900 (KEY)0020036020170000044000300416applicationoftrajectoriesfromgrowthcurveinidentifi DE-627 ger DE-627 rakwb eng 510 DNB Ko, Feng-shou verfasserin aut Application of trajectories from growth curve in identification of longitudinal biomarker for the multivariate survival data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In clinical studies, the researchers measure the patients' response longitudinally. In recent studies, Mixed models are used to determine effects in the individual level. In the other hand, Henderson et al. [3,4] developed a joint likelihood function which combines likelihood functions of longitudinal biomarkers and survival times. They put random effects in the longitudinal component to determine if a longitudinal biomarker is associated with time to an event. In this paper, we deal with a longitudinal biomarker as a growth curve and extend Henderson's method to determine if a longitudinal biomarker is associated with time to an event for the multivariate survival data. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 score test Cox's model Biomarker longitudinal data analysis growth curve Enthalten in Journal of applied statistics Abingdon [u.a.] : Taylor & Francis, 1984 44(2017), 3, Seite 416-11 (DE-627)130678848 (DE-600)882603-1 (DE-576)016221605 0266-4763 nnns volume:44 year:2017 number:3 pages:416-11 http://dx.doi.org/10.1080/02664763.2016.1174196 Volltext http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1174196 http://search.proquest.com/docview/1871598493 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 44 2017 3 416-11 |
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In clinical studies, the researchers measure the patients' response longitudinally. In recent studies, Mixed models are used to determine effects in the individual level. In the other hand, Henderson et al. [3,4] developed a joint likelihood function which combines likelihood functions of longitudinal biomarkers and survival times. They put random effects in the longitudinal component to determine if a longitudinal biomarker is associated with time to an event. In this paper, we deal with a longitudinal biomarker as a growth curve and extend Henderson's method to determine if a longitudinal biomarker is associated with time to an event for the multivariate survival data. |
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In clinical studies, the researchers measure the patients' response longitudinally. In recent studies, Mixed models are used to determine effects in the individual level. In the other hand, Henderson et al. [3,4] developed a joint likelihood function which combines likelihood functions of longitudinal biomarkers and survival times. They put random effects in the longitudinal component to determine if a longitudinal biomarker is associated with time to an event. In this paper, we deal with a longitudinal biomarker as a growth curve and extend Henderson's method to determine if a longitudinal biomarker is associated with time to an event for the multivariate survival data. |
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In clinical studies, the researchers measure the patients' response longitudinally. In recent studies, Mixed models are used to determine effects in the individual level. In the other hand, Henderson et al. [3,4] developed a joint likelihood function which combines likelihood functions of longitudinal biomarkers and survival times. They put random effects in the longitudinal component to determine if a longitudinal biomarker is associated with time to an event. In this paper, we deal with a longitudinal biomarker as a growth curve and extend Henderson's method to determine if a longitudinal biomarker is associated with time to an event for the multivariate survival data. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1990302211</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715031511.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">170303s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/02664763.2016.1174196</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20170301</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1990302211</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1990302211</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1593-c57679f930f26a486e4cc03f3c21d28355d3931952509d3c1b7c486983fab1900</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0020036020170000044000300416applicationoftrajectoriesfromgrowthcurveinidentifi</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ko, Feng-shou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Application of trajectories from growth curve in identification of longitudinal biomarker for the multivariate survival data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In clinical studies, the researchers measure the patients' response longitudinally. 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