Optimal planning of phase II/III programs for clinical trials with multiple endpoints
Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for pha...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kieser, Meinhard [verfasserIn] Kirchner, Marietta - 1985- [verfasserIn] Dölger, Eva [verfasserIn] Götte, Heiko [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
26 April 2018 |
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| Schlagwörter: |
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| Anmerkung: |
Gesehen am 21.10.2019 |
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| Umfang: |
21 |
| Übergeordnetes Werk: |
Enthalten in: Pharmaceutical statistics - New York, NY : Wiley, 2002, 17(2018), 5, Seite 437-457 |
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| Übergeordnetes Werk: |
volume:17 ; year:2018 ; number:5 ; pages:437-457 ; extent:21 |
| Links: |
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| DOI / URN: |
10.1002/pst.1861 |
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| Katalog-ID: |
1679238140 |
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| 520 | |a Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the “all-or-none” and “at-least-one” win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology. | ||
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26 April 2018 |
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2018 |
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10.1002/pst.1861 doi (DE-627)1679238140 (DE-599)KXP1679238140 (OCoLC)1341248231 DE-627 ger DE-627 rda eng Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Optimal planning of phase II/III programs for clinical trials with multiple endpoints Meinhard Kieser, Marietta Kirchner, Eva Dölger, Heiko Götte 26 April 2018 21 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 21.10.2019 Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the “all-or-none” and “at-least-one” win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology. drug development program multiple endpoints phase II/III probability of success sample size Kirchner, Marietta 1985- verfasserin (DE-588)1045114057 (DE-627)773810285 (DE-576)398478910 aut Dölger, Eva verfasserin (DE-588)119756635X (DE-627)1679238000 aut Götte, Heiko verfasserin aut Enthalten in Pharmaceutical statistics New York, NY : Wiley, 2002 17(2018), 5, Seite 437-457 Online-Ressource (DE-627)350784469 (DE-600)2083706-9 (DE-576)306830167 1539-1612 nnns volume:17 year:2018 number:5 pages:437-457 extent:21 https://doi.org/10.1002/pst.1861 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1861 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_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_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_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 17 2018 5 437-457 21 2013 01 DE-16-250 3525744854 00 --%%-- --%%-- --%%-- --%%-- l01 21-10-19 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_4 2013 01 DE-16-250 03 s s_21 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)1572139854 Kirchner, Marietta 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 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)1679238051 Dölger, Eva 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/pst.1861 doi (DE-627)1679238140 (DE-599)KXP1679238140 (OCoLC)1341248231 DE-627 ger DE-627 rda eng Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Optimal planning of phase II/III programs for clinical trials with multiple endpoints Meinhard Kieser, Marietta Kirchner, Eva Dölger, Heiko Götte 26 April 2018 21 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 21.10.2019 Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the “all-or-none” and “at-least-one” win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology. drug development program multiple endpoints phase II/III probability of success sample size Kirchner, Marietta 1985- verfasserin (DE-588)1045114057 (DE-627)773810285 (DE-576)398478910 aut Dölger, Eva verfasserin (DE-588)119756635X (DE-627)1679238000 aut Götte, Heiko verfasserin aut Enthalten in Pharmaceutical statistics New York, NY : Wiley, 2002 17(2018), 5, Seite 437-457 Online-Ressource (DE-627)350784469 (DE-600)2083706-9 (DE-576)306830167 1539-1612 nnns volume:17 year:2018 number:5 pages:437-457 extent:21 https://doi.org/10.1002/pst.1861 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1861 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_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_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_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 17 2018 5 437-457 21 2013 01 DE-16-250 3525744854 00 --%%-- --%%-- --%%-- --%%-- l01 21-10-19 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_4 2013 01 DE-16-250 03 s s_21 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)1572139854 Kirchner, Marietta 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 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)1679238051 Dölger, Eva 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/pst.1861 doi (DE-627)1679238140 (DE-599)KXP1679238140 (OCoLC)1341248231 DE-627 ger DE-627 rda eng Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Optimal planning of phase II/III programs for clinical trials with multiple endpoints Meinhard Kieser, Marietta Kirchner, Eva Dölger, Heiko Götte 26 April 2018 21 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 21.10.2019 Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the “all-or-none” and “at-least-one” win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology. drug development program multiple endpoints phase II/III probability of success sample size Kirchner, Marietta 1985- verfasserin (DE-588)1045114057 (DE-627)773810285 (DE-576)398478910 aut Dölger, Eva verfasserin (DE-588)119756635X (DE-627)1679238000 aut Götte, Heiko verfasserin aut Enthalten in Pharmaceutical statistics New York, NY : Wiley, 2002 17(2018), 5, Seite 437-457 Online-Ressource (DE-627)350784469 (DE-600)2083706-9 (DE-576)306830167 1539-1612 nnns volume:17 year:2018 number:5 pages:437-457 extent:21 https://doi.org/10.1002/pst.1861 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1861 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_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_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_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 17 2018 5 437-457 21 2013 01 DE-16-250 3525744854 00 --%%-- --%%-- --%%-- --%%-- l01 21-10-19 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_4 2013 01 DE-16-250 03 s s_21 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)1572139854 Kirchner, Marietta 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 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)1679238051 Dölger, Eva 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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optimal planning of phase ii/iii programs for clinical trials with multiple endpoints |
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Optimal planning of phase II/III programs for clinical trials with multiple endpoints |
| abstract |
Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the “all-or-none” and “at-least-one” win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology. Gesehen am 21.10.2019 |
| abstractGer |
Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the “all-or-none” and “at-least-one” win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology. Gesehen am 21.10.2019 |
| abstract_unstemmed |
Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the “all-or-none” and “at-least-one” win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology. Gesehen am 21.10.2019 |
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Optimal planning of phase II/III programs for clinical trials with multiple endpoints |
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