Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data
The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MC...
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
The Berkeley Electronic Press ; 2008 |
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| Reproduktion: |
Berkeley Electronic Press Academic Journals |
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| Übergeordnetes Werk: |
In: The international journal of biostatistics - Berkeley, Calif. : BePress, 2005, 4(2008), 1, Seite 1 |
| Übergeordnetes Werk: |
volume:4 ; year:2008 ; number:1 ; pages:1 |
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| 520 | |a The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings. | ||
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(DE-627)NLEJ219555532 DE-627 ger DE-627 rakwb eng XD-US Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data The Berkeley Electronic Press 2008 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings. Berkeley Electronic Press Academic Journals AUC bias missing data test Clinical Trials General Biostatistics Longitudinal Data Analysis and Time Series Spritzler, John oth DeGruttola, Victor G oth Pei, Lixia oth In The international journal of biostatistics Berkeley, Calif. : BePress, 2005 4(2008), 1, Seite 1 Online-Ressource (DE-627)NLEJ219537038 (DE-600)2239443-6 1557-4679 nnns volume:4 year:2008 number:1 pages:1 http://www.bepress.com/ijb/vol4/iss1/1 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 4 2008 1 1 |
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(DE-627)NLEJ219555532 DE-627 ger DE-627 rakwb eng XD-US Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data The Berkeley Electronic Press 2008 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings. Berkeley Electronic Press Academic Journals AUC bias missing data test Clinical Trials General Biostatistics Longitudinal Data Analysis and Time Series Spritzler, John oth DeGruttola, Victor G oth Pei, Lixia oth In The international journal of biostatistics Berkeley, Calif. : BePress, 2005 4(2008), 1, Seite 1 Online-Ressource (DE-627)NLEJ219537038 (DE-600)2239443-6 1557-4679 nnns volume:4 year:2008 number:1 pages:1 http://www.bepress.com/ijb/vol4/iss1/1 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 4 2008 1 1 |
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two-sample tests of area-under-the-curve in the presence of missing data |
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Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data |
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The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings. |
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The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings. |
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The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ219555532</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707085714.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">090716s2008 xxu|||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ219555532</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="044" ind1=" " ind2=" "><subfield code="c">XD-US</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Two-Sample Tests of Area-Under-the-Curve in the Presence of Missing Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="b">The Berkeley Electronic Press</subfield><subfield code="c">2008</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">Berkeley Electronic Press Academic Journals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">AUC</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bias</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">missing data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">test</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clinical Trials</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">General Biostatistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Longitudinal Data Analysis and Time Series</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Spritzler, John</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">DeGruttola, Victor G</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pei, Lixia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">The international journal of biostatistics</subfield><subfield code="d">Berkeley, Calif. : BePress, 2005</subfield><subfield code="g">4(2008), 1, Seite 1</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ219537038</subfield><subfield code="w">(DE-600)2239443-6</subfield><subfield code="x">1557-4679</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2008</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:1</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.bepress.com/ijb/vol4/iss1/1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-BEP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2008</subfield><subfield code="e">1</subfield><subfield code="h">1</subfield></datafield></record></collection>
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