Assessment of statistical significance and clinical relevance
In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kieser, Meinhard [verfasserIn] Friede, Tim - 1971- [verfasserIn] Gondan, Matthias [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
First published: 28 September 2012 Gesehen am 23.08.2018 |
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| Umfang: |
13 |
| Übergeordnetes Werk: |
Enthalten in: Statistics in medicine - Chichester [u.a.] : Wiley, 1982, 32(2013), 10, Seite 1707-1719 |
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| Übergeordnetes Werk: |
volume:32 ; year:2013 ; number:10 ; pages:1707-1719 ; extent:13 |
| Links: |
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| DOI / URN: |
10.1002/sim.5634 |
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| Katalog-ID: |
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| 520 | |a In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. Copyright © 2012 John Wiley & Sons, Ltd. | ||
| 650 | 4 | |a clinical relevance | |
| 650 | 4 | |a effect size | |
| 650 | 4 | |a probabilistic index | |
| 650 | 4 | |a relative effect | |
| 650 | 4 | |a responder analysis | |
| 650 | 4 | |a sample size | |
| 650 | 4 | |a statistical significance | |
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10.1002/sim.5634 doi (DE-627)158032990X (DE-576)51032990X (DE-599)BSZ51032990X (OCoLC)1341017552 DE-627 ger DE-627 rda eng Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Assessment of statistical significance and clinical relevance Meinhard Kieser, Tim Friede, Matthias Gondan 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 28 September 2012 Gesehen am 23.08.2018 In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. Copyright © 2012 John Wiley & Sons, Ltd. clinical relevance effect size probabilistic index relative effect responder analysis sample size statistical significance Friede, Tim 1971- verfasserin (DE-588)123130999 (DE-627)082365156 (DE-576)183804082 aut Gondan, Matthias verfasserin (DE-588)103832159X (DE-627)757123074 (DE-576)181338165 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 32(2013), 10, Seite 1707-1719 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:32 year:2013 number:10 pages:1707-1719 extent:13 http://dx.doi.org/10.1002/sim.5634 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5634 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_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 AR 32 2013 10 1707-1719 13 2013 01 DE-16-250 3023012563 00 --%%-- --%%-- --%%-- --%%-- l01 23-08-18 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1496459253 Friede, Tim 2013 01 DE-16-250 05 k (DE-627)1416822720 Extern 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 2013 01 DE-16-250 06 p (DE-627)146230382X Gondan, Matthias 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
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10.1002/sim.5634 doi (DE-627)158032990X (DE-576)51032990X (DE-599)BSZ51032990X (OCoLC)1341017552 DE-627 ger DE-627 rda eng Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Assessment of statistical significance and clinical relevance Meinhard Kieser, Tim Friede, Matthias Gondan 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 28 September 2012 Gesehen am 23.08.2018 In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. Copyright © 2012 John Wiley & Sons, Ltd. clinical relevance effect size probabilistic index relative effect responder analysis sample size statistical significance Friede, Tim 1971- verfasserin (DE-588)123130999 (DE-627)082365156 (DE-576)183804082 aut Gondan, Matthias verfasserin (DE-588)103832159X (DE-627)757123074 (DE-576)181338165 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 32(2013), 10, Seite 1707-1719 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:32 year:2013 number:10 pages:1707-1719 extent:13 http://dx.doi.org/10.1002/sim.5634 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5634 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_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 AR 32 2013 10 1707-1719 13 2013 01 DE-16-250 3023012563 00 --%%-- --%%-- --%%-- --%%-- l01 23-08-18 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1496459253 Friede, Tim 2013 01 DE-16-250 05 k (DE-627)1416822720 Extern 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 2013 01 DE-16-250 06 p (DE-627)146230382X Gondan, Matthias 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
| allfields_unstemmed |
10.1002/sim.5634 doi (DE-627)158032990X (DE-576)51032990X (DE-599)BSZ51032990X (OCoLC)1341017552 DE-627 ger DE-627 rda eng Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Assessment of statistical significance and clinical relevance Meinhard Kieser, Tim Friede, Matthias Gondan 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 28 September 2012 Gesehen am 23.08.2018 In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. Copyright © 2012 John Wiley & Sons, Ltd. clinical relevance effect size probabilistic index relative effect responder analysis sample size statistical significance Friede, Tim 1971- verfasserin (DE-588)123130999 (DE-627)082365156 (DE-576)183804082 aut Gondan, Matthias verfasserin (DE-588)103832159X (DE-627)757123074 (DE-576)181338165 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 32(2013), 10, Seite 1707-1719 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:32 year:2013 number:10 pages:1707-1719 extent:13 http://dx.doi.org/10.1002/sim.5634 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5634 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_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 AR 32 2013 10 1707-1719 13 2013 01 DE-16-250 3023012563 00 --%%-- --%%-- --%%-- --%%-- l01 23-08-18 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1496459253 Friede, Tim 2013 01 DE-16-250 05 k (DE-627)1416822720 Extern 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 2013 01 DE-16-250 06 p (DE-627)146230382X Gondan, Matthias 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
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10.1002/sim.5634 doi (DE-627)158032990X (DE-576)51032990X (DE-599)BSZ51032990X (OCoLC)1341017552 DE-627 ger DE-627 rda eng Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Assessment of statistical significance and clinical relevance Meinhard Kieser, Tim Friede, Matthias Gondan 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 28 September 2012 Gesehen am 23.08.2018 In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. Copyright © 2012 John Wiley & Sons, Ltd. clinical relevance effect size probabilistic index relative effect responder analysis sample size statistical significance Friede, Tim 1971- verfasserin (DE-588)123130999 (DE-627)082365156 (DE-576)183804082 aut Gondan, Matthias verfasserin (DE-588)103832159X (DE-627)757123074 (DE-576)181338165 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 32(2013), 10, Seite 1707-1719 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:32 year:2013 number:10 pages:1707-1719 extent:13 http://dx.doi.org/10.1002/sim.5634 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5634 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_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 AR 32 2013 10 1707-1719 13 2013 01 DE-16-250 3023012563 00 --%%-- --%%-- --%%-- --%%-- l01 23-08-18 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1496459253 Friede, Tim 2013 01 DE-16-250 05 k (DE-627)1416822720 Extern 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 2013 01 DE-16-250 06 p (DE-627)146230382X Gondan, Matthias 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
| allfieldsSound |
10.1002/sim.5634 doi (DE-627)158032990X (DE-576)51032990X (DE-599)BSZ51032990X (OCoLC)1341017552 DE-627 ger DE-627 rda eng Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Assessment of statistical significance and clinical relevance Meinhard Kieser, Tim Friede, Matthias Gondan 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 28 September 2012 Gesehen am 23.08.2018 In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. Copyright © 2012 John Wiley & Sons, Ltd. clinical relevance effect size probabilistic index relative effect responder analysis sample size statistical significance Friede, Tim 1971- verfasserin (DE-588)123130999 (DE-627)082365156 (DE-576)183804082 aut Gondan, Matthias verfasserin (DE-588)103832159X (DE-627)757123074 (DE-576)181338165 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 32(2013), 10, Seite 1707-1719 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:32 year:2013 number:10 pages:1707-1719 extent:13 http://dx.doi.org/10.1002/sim.5634 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5634 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_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 AR 32 2013 10 1707-1719 13 2013 01 DE-16-250 3023012563 00 --%%-- --%%-- --%%-- --%%-- l01 23-08-18 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1496459253 Friede, Tim 2013 01 DE-16-250 05 k (DE-627)1416822720 Extern 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 2013 01 DE-16-250 06 p (DE-627)146230382X Gondan, Matthias 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
| language |
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Enthalten in Statistics in medicine 32(2013), 10, Seite 1707-1719 volume:32 year:2013 number:10 pages:1707-1719 extent:13 |
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Kieser, Meinhard @@aut@@ Friede, Tim @@aut@@ Gondan, Matthias @@aut@@ |
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In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. Copyright © 2012 John Wiley & Sons, Ltd. First published: 28 September 2012 Gesehen am 23.08.2018 |
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In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. Copyright © 2012 John Wiley & Sons, Ltd. First published: 28 September 2012 Gesehen am 23.08.2018 |
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Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach. 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