A flexible semiparametric transformation model for survival data
Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Scheike, Thomas H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2006 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Lifetime data analysis - [S.l.] : Proquest, 1995, 12(2006), 4 vom: 20. Sept., Seite 461-480 |
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| Übergeordnetes Werk: |
volume:12 ; year:2006 ; number:4 ; day:20 ; month:09 ; pages:461-480 |
| Links: |
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| DOI / URN: |
10.1007/s10985-006-9021-1 |
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| Katalog-ID: |
SPR015363554 |
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| 100 | 1 | |a Scheike, Thomas H. |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A flexible semiparametric transformation model for survival data |
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| 520 | |a Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested. | ||
| 650 | 4 | |a Estimating equation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Flexible modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Linear transformation model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Proportional odds model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Semiparametric modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Survival data |7 (dpeaa)DE-He213 | |
| 773 | 0 | 8 | |i Enthalten in |t Lifetime data analysis |d [S.l.] : Proquest, 1995 |g 12(2006), 4 vom: 20. Sept., Seite 461-480 |w (DE-627)271348410 |w (DE-600)1479719-7 |x 1572-9249 |7 nnns |
| 773 | 1 | 8 | |g volume:12 |g year:2006 |g number:4 |g day:20 |g month:09 |g pages:461-480 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s10985-006-9021-1 |z lizenzpflichtig |3 Volltext |
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| 912 | |a GBV_ILN_2106 | ||
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| 912 | |a GBV_ILN_2110 | ||
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| 912 | |a GBV_ILN_2113 | ||
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| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2119 | ||
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| 912 | |a GBV_ILN_4313 | ||
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| 912 | |a GBV_ILN_4324 | ||
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| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4336 | ||
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| 952 | |d 12 |j 2006 |e 4 |b 20 |c 09 |h 461-480 | ||
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2006 |
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31.73 44.32 |
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2006 |
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10.1007/s10985-006-9021-1 doi (DE-627)SPR015363554 (SPR)s10985-006-9021-1-e DE-627 ger DE-627 rakwb eng 510 004 ASE 31.73 bkl 44.32 bkl Scheike, Thomas H. verfasserin aut A flexible semiparametric transformation model for survival data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested. Estimating equation (dpeaa)DE-He213 Flexible modeling (dpeaa)DE-He213 Linear transformation model (dpeaa)DE-He213 Proportional odds model (dpeaa)DE-He213 Semiparametric modeling (dpeaa)DE-He213 Survival data (dpeaa)DE-He213 Enthalten in Lifetime data analysis [S.l.] : Proquest, 1995 12(2006), 4 vom: 20. Sept., Seite 461-480 (DE-627)271348410 (DE-600)1479719-7 1572-9249 nnns volume:12 year:2006 number:4 day:20 month:09 pages:461-480 https://dx.doi.org/10.1007/s10985-006-9021-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.73 ASE 44.32 ASE AR 12 2006 4 20 09 461-480 |
| spelling |
10.1007/s10985-006-9021-1 doi (DE-627)SPR015363554 (SPR)s10985-006-9021-1-e DE-627 ger DE-627 rakwb eng 510 004 ASE 31.73 bkl 44.32 bkl Scheike, Thomas H. verfasserin aut A flexible semiparametric transformation model for survival data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested. Estimating equation (dpeaa)DE-He213 Flexible modeling (dpeaa)DE-He213 Linear transformation model (dpeaa)DE-He213 Proportional odds model (dpeaa)DE-He213 Semiparametric modeling (dpeaa)DE-He213 Survival data (dpeaa)DE-He213 Enthalten in Lifetime data analysis [S.l.] : Proquest, 1995 12(2006), 4 vom: 20. Sept., Seite 461-480 (DE-627)271348410 (DE-600)1479719-7 1572-9249 nnns volume:12 year:2006 number:4 day:20 month:09 pages:461-480 https://dx.doi.org/10.1007/s10985-006-9021-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.73 ASE 44.32 ASE AR 12 2006 4 20 09 461-480 |
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10.1007/s10985-006-9021-1 doi (DE-627)SPR015363554 (SPR)s10985-006-9021-1-e DE-627 ger DE-627 rakwb eng 510 004 ASE 31.73 bkl 44.32 bkl Scheike, Thomas H. verfasserin aut A flexible semiparametric transformation model for survival data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested. Estimating equation (dpeaa)DE-He213 Flexible modeling (dpeaa)DE-He213 Linear transformation model (dpeaa)DE-He213 Proportional odds model (dpeaa)DE-He213 Semiparametric modeling (dpeaa)DE-He213 Survival data (dpeaa)DE-He213 Enthalten in Lifetime data analysis [S.l.] : Proquest, 1995 12(2006), 4 vom: 20. Sept., Seite 461-480 (DE-627)271348410 (DE-600)1479719-7 1572-9249 nnns volume:12 year:2006 number:4 day:20 month:09 pages:461-480 https://dx.doi.org/10.1007/s10985-006-9021-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.73 ASE 44.32 ASE AR 12 2006 4 20 09 461-480 |
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10.1007/s10985-006-9021-1 doi (DE-627)SPR015363554 (SPR)s10985-006-9021-1-e DE-627 ger DE-627 rakwb eng 510 004 ASE 31.73 bkl 44.32 bkl Scheike, Thomas H. verfasserin aut A flexible semiparametric transformation model for survival data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested. Estimating equation (dpeaa)DE-He213 Flexible modeling (dpeaa)DE-He213 Linear transformation model (dpeaa)DE-He213 Proportional odds model (dpeaa)DE-He213 Semiparametric modeling (dpeaa)DE-He213 Survival data (dpeaa)DE-He213 Enthalten in Lifetime data analysis [S.l.] : Proquest, 1995 12(2006), 4 vom: 20. Sept., Seite 461-480 (DE-627)271348410 (DE-600)1479719-7 1572-9249 nnns volume:12 year:2006 number:4 day:20 month:09 pages:461-480 https://dx.doi.org/10.1007/s10985-006-9021-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.73 ASE 44.32 ASE AR 12 2006 4 20 09 461-480 |
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Enthalten in Lifetime data analysis 12(2006), 4 vom: 20. Sept., Seite 461-480 volume:12 year:2006 number:4 day:20 month:09 pages:461-480 |
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| author |
Scheike, Thomas H. |
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Scheike, Thomas H. ddc 510 bkl 31.73 bkl 44.32 misc Estimating equation misc Flexible modeling misc Linear transformation model misc Proportional odds model misc Semiparametric modeling misc Survival data A flexible semiparametric transformation model for survival data |
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510 004 ASE 31.73 bkl 44.32 bkl A flexible semiparametric transformation model for survival data Estimating equation (dpeaa)DE-He213 Flexible modeling (dpeaa)DE-He213 Linear transformation model (dpeaa)DE-He213 Proportional odds model (dpeaa)DE-He213 Semiparametric modeling (dpeaa)DE-He213 Survival data (dpeaa)DE-He213 |
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ddc 510 bkl 31.73 bkl 44.32 misc Estimating equation misc Flexible modeling misc Linear transformation model misc Proportional odds model misc Semiparametric modeling misc Survival data |
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ddc 510 bkl 31.73 bkl 44.32 misc Estimating equation misc Flexible modeling misc Linear transformation model misc Proportional odds model misc Semiparametric modeling misc Survival data |
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A flexible semiparametric transformation model for survival data |
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flexible semiparametric transformation model for survival data |
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A flexible semiparametric transformation model for survival data |
| abstract |
Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested. |
| abstractGer |
Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested. |
| abstract_unstemmed |
Abstract I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested. |
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| title_short |
A flexible semiparametric transformation model for survival data |
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https://dx.doi.org/10.1007/s10985-006-9021-1 |
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| doi_str |
10.1007/s10985-006-9021-1 |
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2024-07-03T15:44:42.647Z |
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