Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India
Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vivekanandan, Kalaiselvan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Drug Information Association, Inc 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Therapeutic innovation & regulatory science - [New York] : Springer Nature, 2013, 49(2015), 6 vom: Nov., Seite 898-902 |
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| Übergeordnetes Werk: |
volume:49 ; year:2015 ; number:6 ; month:11 ; pages:898-902 |
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| DOI / URN: |
10.1177/2168479015589822 |
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SPR03892577X |
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| 520 | |a Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. | ||
| 650 | 4 | |a pharmacovigilance |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a signal detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bayesian |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tripathi, Arunabh |4 aut | |
| 700 | 1 | |a Saurabh, Archana |4 aut | |
| 700 | 1 | |a Kumar, Ranvir |4 aut | |
| 700 | 1 | |a Kumar, Rishi |4 aut | |
| 700 | 1 | |a Prasad, Thota |4 aut | |
| 700 | 1 | |a Singh, Gyanendra Nath |4 aut | |
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10.1177/2168479015589822 doi (DE-627)SPR03892577X (SPR)2168479015589822-e DE-627 ger DE-627 rakwb eng Vivekanandan, Kalaiselvan verfasserin aut Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2015 Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. pharmacovigilance (dpeaa)DE-He213 signal detection (dpeaa)DE-He213 Bayesian (dpeaa)DE-He213 Tripathi, Arunabh aut Saurabh, Archana aut Kumar, Ranvir aut Kumar, Rishi aut Prasad, Thota aut Singh, Gyanendra Nath aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 49(2015), 6 vom: Nov., Seite 898-902 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:49 year:2015 number:6 month:11 pages:898-902 https://dx.doi.org/10.1177/2168479015589822 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2098 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_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2193 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2704 GBV_ILN_2707 GBV_ILN_2889 GBV_ILN_2890 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 49 2015 6 11 898-902 |
| spelling |
10.1177/2168479015589822 doi (DE-627)SPR03892577X (SPR)2168479015589822-e DE-627 ger DE-627 rakwb eng Vivekanandan, Kalaiselvan verfasserin aut Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2015 Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. pharmacovigilance (dpeaa)DE-He213 signal detection (dpeaa)DE-He213 Bayesian (dpeaa)DE-He213 Tripathi, Arunabh aut Saurabh, Archana aut Kumar, Ranvir aut Kumar, Rishi aut Prasad, Thota aut Singh, Gyanendra Nath aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 49(2015), 6 vom: Nov., Seite 898-902 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:49 year:2015 number:6 month:11 pages:898-902 https://dx.doi.org/10.1177/2168479015589822 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2098 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_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2193 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2704 GBV_ILN_2707 GBV_ILN_2889 GBV_ILN_2890 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 49 2015 6 11 898-902 |
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10.1177/2168479015589822 doi (DE-627)SPR03892577X (SPR)2168479015589822-e DE-627 ger DE-627 rakwb eng Vivekanandan, Kalaiselvan verfasserin aut Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2015 Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. pharmacovigilance (dpeaa)DE-He213 signal detection (dpeaa)DE-He213 Bayesian (dpeaa)DE-He213 Tripathi, Arunabh aut Saurabh, Archana aut Kumar, Ranvir aut Kumar, Rishi aut Prasad, Thota aut Singh, Gyanendra Nath aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 49(2015), 6 vom: Nov., Seite 898-902 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:49 year:2015 number:6 month:11 pages:898-902 https://dx.doi.org/10.1177/2168479015589822 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2098 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_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2193 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2704 GBV_ILN_2707 GBV_ILN_2889 GBV_ILN_2890 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 49 2015 6 11 898-902 |
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10.1177/2168479015589822 doi (DE-627)SPR03892577X (SPR)2168479015589822-e DE-627 ger DE-627 rakwb eng Vivekanandan, Kalaiselvan verfasserin aut Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2015 Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. pharmacovigilance (dpeaa)DE-He213 signal detection (dpeaa)DE-He213 Bayesian (dpeaa)DE-He213 Tripathi, Arunabh aut Saurabh, Archana aut Kumar, Ranvir aut Kumar, Rishi aut Prasad, Thota aut Singh, Gyanendra Nath aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 49(2015), 6 vom: Nov., Seite 898-902 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:49 year:2015 number:6 month:11 pages:898-902 https://dx.doi.org/10.1177/2168479015589822 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2098 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_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2193 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2704 GBV_ILN_2707 GBV_ILN_2889 GBV_ILN_2890 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 49 2015 6 11 898-902 |
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10.1177/2168479015589822 doi (DE-627)SPR03892577X (SPR)2168479015589822-e DE-627 ger DE-627 rakwb eng Vivekanandan, Kalaiselvan verfasserin aut Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2015 Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. pharmacovigilance (dpeaa)DE-He213 signal detection (dpeaa)DE-He213 Bayesian (dpeaa)DE-He213 Tripathi, Arunabh aut Saurabh, Archana aut Kumar, Ranvir aut Kumar, Rishi aut Prasad, Thota aut Singh, Gyanendra Nath aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 49(2015), 6 vom: Nov., Seite 898-902 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:49 year:2015 number:6 month:11 pages:898-902 https://dx.doi.org/10.1177/2168479015589822 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2098 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_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2193 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2704 GBV_ILN_2707 GBV_ILN_2889 GBV_ILN_2890 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_4277 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_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 49 2015 6 11 898-902 |
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Vivekanandan, Kalaiselvan @@aut@@ Tripathi, Arunabh @@aut@@ Saurabh, Archana @@aut@@ Kumar, Ranvir @@aut@@ Kumar, Rishi @@aut@@ Prasad, Thota @@aut@@ Singh, Gyanendra Nath @@aut@@ |
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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">SPR03892577X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519124311.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1177/2168479015589822</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR03892577X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)2168479015589822-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">Vivekanandan, Kalaiselvan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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">© Drug Information Association, Inc 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. 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Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India pharmacovigilance (dpeaa)DE-He213 signal detection (dpeaa)DE-He213 Bayesian (dpeaa)DE-He213 |
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Vivekanandan, Kalaiselvan Tripathi, Arunabh Saurabh, Archana Kumar, Ranvir Kumar, Rishi Prasad, Thota Singh, Gyanendra Nath |
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quantitative methods for the identification of signals for individual case safety reports in india |
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Quantitative Methods for the Identification of Signals for Individual Case Safety Reports in India |
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Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. © Drug Information Association, Inc 2015 |
| abstractGer |
Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. © Drug Information Association, Inc 2015 |
| abstract_unstemmed |
Background The Pharmacovigilance Programme of India (PvPI) is responsible for collecting reports of adverse drug reactions (ADRs) to assess the association between particular drugs and ADRs. The aim of the present study was to apply statistical tools to determine associations between drugs and ADRs for signal detection in the PvPI. Methods Four methods were proposed for quantitative signal detection: one was based on Bayesian inference and others on classical inference procedures. The effectiveness of the proposed methods was assessed by applying them to 4 drug-ADR combinations. Results The proposed methods were easy to apply and relevant to the Indian context. In selected methods, the information component value was more specific, whereas the proportional relative risk was more sensitive. Conclusions The proposed methods may help in the identification of new signals in Indian individual case safety reports. © Drug Information Association, Inc 2015 |
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| score |
7.3982077 |