Bayesian Analysis of Multivariate Matched Proportions with Sparse Response
Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same sub...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Meyer, Mark J. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Statistics in biosciences - New York, NY : Springer, 2009, 15(2023), 2 vom: 30. März, Seite 490-509 |
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| Übergeordnetes Werk: |
volume:15 ; year:2023 ; number:2 ; day:30 ; month:03 ; pages:490-509 |
| Links: |
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| DOI / URN: |
10.1007/s12561-023-09368-8 |
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| Katalog-ID: |
SPR051875071 |
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| 520 | |a Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. | ||
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| 650 | 4 | |a Penalized Bayesian regression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Systems of care |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Pediatric mental and behavioral disorders |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cheng, Haobo |4 aut | |
| 700 | 1 | |a Knutson, Katherine Hobbs |4 aut | |
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10.1007/s12561-023-09368-8 doi (DE-627)SPR051875071 (SPR)s12561-023-09368-8-e DE-627 ger DE-627 rakwb eng Meyer, Mark J. verfasserin (orcid)0000-0003-3942-9675 aut Bayesian Analysis of Multivariate Matched Proportions with Sparse Response 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. Multivariate probit regression (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Bayesian FPCA (dpeaa)DE-He213 Penalized Bayesian regression (dpeaa)DE-He213 Systems of care (dpeaa)DE-He213 Pediatric mental and behavioral disorders (dpeaa)DE-He213 Cheng, Haobo aut Knutson, Katherine Hobbs aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 15(2023), 2 vom: 30. März, Seite 490-509 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:15 year:2023 number:2 day:30 month:03 pages:490-509 https://dx.doi.org/10.1007/s12561-023-09368-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 2 30 03 490-509 |
| spelling |
10.1007/s12561-023-09368-8 doi (DE-627)SPR051875071 (SPR)s12561-023-09368-8-e DE-627 ger DE-627 rakwb eng Meyer, Mark J. verfasserin (orcid)0000-0003-3942-9675 aut Bayesian Analysis of Multivariate Matched Proportions with Sparse Response 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. Multivariate probit regression (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Bayesian FPCA (dpeaa)DE-He213 Penalized Bayesian regression (dpeaa)DE-He213 Systems of care (dpeaa)DE-He213 Pediatric mental and behavioral disorders (dpeaa)DE-He213 Cheng, Haobo aut Knutson, Katherine Hobbs aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 15(2023), 2 vom: 30. März, Seite 490-509 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:15 year:2023 number:2 day:30 month:03 pages:490-509 https://dx.doi.org/10.1007/s12561-023-09368-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 2 30 03 490-509 |
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10.1007/s12561-023-09368-8 doi (DE-627)SPR051875071 (SPR)s12561-023-09368-8-e DE-627 ger DE-627 rakwb eng Meyer, Mark J. verfasserin (orcid)0000-0003-3942-9675 aut Bayesian Analysis of Multivariate Matched Proportions with Sparse Response 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. Multivariate probit regression (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Bayesian FPCA (dpeaa)DE-He213 Penalized Bayesian regression (dpeaa)DE-He213 Systems of care (dpeaa)DE-He213 Pediatric mental and behavioral disorders (dpeaa)DE-He213 Cheng, Haobo aut Knutson, Katherine Hobbs aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 15(2023), 2 vom: 30. März, Seite 490-509 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:15 year:2023 number:2 day:30 month:03 pages:490-509 https://dx.doi.org/10.1007/s12561-023-09368-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 2 30 03 490-509 |
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10.1007/s12561-023-09368-8 doi (DE-627)SPR051875071 (SPR)s12561-023-09368-8-e DE-627 ger DE-627 rakwb eng Meyer, Mark J. verfasserin (orcid)0000-0003-3942-9675 aut Bayesian Analysis of Multivariate Matched Proportions with Sparse Response 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. Multivariate probit regression (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Bayesian FPCA (dpeaa)DE-He213 Penalized Bayesian regression (dpeaa)DE-He213 Systems of care (dpeaa)DE-He213 Pediatric mental and behavioral disorders (dpeaa)DE-He213 Cheng, Haobo aut Knutson, Katherine Hobbs aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 15(2023), 2 vom: 30. März, Seite 490-509 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:15 year:2023 number:2 day:30 month:03 pages:490-509 https://dx.doi.org/10.1007/s12561-023-09368-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 2 30 03 490-509 |
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10.1007/s12561-023-09368-8 doi (DE-627)SPR051875071 (SPR)s12561-023-09368-8-e DE-627 ger DE-627 rakwb eng Meyer, Mark J. verfasserin (orcid)0000-0003-3942-9675 aut Bayesian Analysis of Multivariate Matched Proportions with Sparse Response 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. Multivariate probit regression (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Bayesian FPCA (dpeaa)DE-He213 Penalized Bayesian regression (dpeaa)DE-He213 Systems of care (dpeaa)DE-He213 Pediatric mental and behavioral disorders (dpeaa)DE-He213 Cheng, Haobo aut Knutson, Katherine Hobbs aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 15(2023), 2 vom: 30. März, Seite 490-509 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:15 year:2023 number:2 day:30 month:03 pages:490-509 https://dx.doi.org/10.1007/s12561-023-09368-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 2 30 03 490-509 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR051875071</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230613064738.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230613s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12561-023-09368-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051875071</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12561-023-09368-8-e</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="100" ind1="1" ind2=" "><subfield code="a">Meyer, Mark J.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3942-9675</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian Analysis of Multivariate Matched Proportions with Sparse Response</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. 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Meyer, Mark J. |
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bayesian analysis of multivariate matched proportions with sparse response |
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Bayesian Analysis of Multivariate Matched Proportions with Sparse Response |
| abstract |
Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. © The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. © The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed. © The Author(s) under exclusive licence to International Chinese Statistical Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Bayesian Analysis of Multivariate Matched Proportions with Sparse Response |
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Cheng, Haobo Knutson, Katherine Hobbs |
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10.1007/s12561-023-09368-8 |
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| score |
7.400649 |