Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations
Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Marchenko, Olga [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: |
© Drug Information Association, Inc 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Therapeutic innovation & regulatory science - [New York] : Springer Nature, 2013, 47(2013), 5 vom: Sept., Seite 602-612 |
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| Übergeordnetes Werk: |
volume:47 ; year:2013 ; number:5 ; month:09 ; pages:602-612 |
| Links: |
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| DOI / URN: |
10.1177/2168479013495685 |
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SPR03892319X |
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| 520 | |a Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. | ||
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| 650 | 4 | |a program success |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a adaptive design |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Miller, Joel |4 aut | |
| 700 | 1 | |a Parke, Tom |4 aut | |
| 700 | 1 | |a Perevozskaya, Inna |4 aut | |
| 700 | 1 | |a Qian, Jiang |4 aut | |
| 700 | 1 | |a Wang, Yanping |4 aut | |
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10.1177/2168479013495685 doi (DE-627)SPR03892319X (SPR)2168479013495685-e DE-627 ger DE-627 rakwb eng Marchenko, Olga verfasserin aut Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2013 Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. innovative designs (dpeaa)DE-He213 program optimization (dpeaa)DE-He213 eNPV (dpeaa)DE-He213 program success (dpeaa)DE-He213 adaptive design (dpeaa)DE-He213 Miller, Joel aut Parke, Tom aut Perevozskaya, Inna aut Qian, Jiang aut Wang, Yanping aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 47(2013), 5 vom: Sept., Seite 602-612 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:47 year:2013 number:5 month:09 pages:602-612 https://dx.doi.org/10.1177/2168479013495685 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 47 2013 5 09 602-612 |
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10.1177/2168479013495685 doi (DE-627)SPR03892319X (SPR)2168479013495685-e DE-627 ger DE-627 rakwb eng Marchenko, Olga verfasserin aut Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2013 Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. innovative designs (dpeaa)DE-He213 program optimization (dpeaa)DE-He213 eNPV (dpeaa)DE-He213 program success (dpeaa)DE-He213 adaptive design (dpeaa)DE-He213 Miller, Joel aut Parke, Tom aut Perevozskaya, Inna aut Qian, Jiang aut Wang, Yanping aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 47(2013), 5 vom: Sept., Seite 602-612 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:47 year:2013 number:5 month:09 pages:602-612 https://dx.doi.org/10.1177/2168479013495685 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 47 2013 5 09 602-612 |
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10.1177/2168479013495685 doi (DE-627)SPR03892319X (SPR)2168479013495685-e DE-627 ger DE-627 rakwb eng Marchenko, Olga verfasserin aut Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2013 Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. innovative designs (dpeaa)DE-He213 program optimization (dpeaa)DE-He213 eNPV (dpeaa)DE-He213 program success (dpeaa)DE-He213 adaptive design (dpeaa)DE-He213 Miller, Joel aut Parke, Tom aut Perevozskaya, Inna aut Qian, Jiang aut Wang, Yanping aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 47(2013), 5 vom: Sept., Seite 602-612 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:47 year:2013 number:5 month:09 pages:602-612 https://dx.doi.org/10.1177/2168479013495685 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 47 2013 5 09 602-612 |
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10.1177/2168479013495685 doi (DE-627)SPR03892319X (SPR)2168479013495685-e DE-627 ger DE-627 rakwb eng Marchenko, Olga verfasserin aut Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2013 Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. innovative designs (dpeaa)DE-He213 program optimization (dpeaa)DE-He213 eNPV (dpeaa)DE-He213 program success (dpeaa)DE-He213 adaptive design (dpeaa)DE-He213 Miller, Joel aut Parke, Tom aut Perevozskaya, Inna aut Qian, Jiang aut Wang, Yanping aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 47(2013), 5 vom: Sept., Seite 602-612 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:47 year:2013 number:5 month:09 pages:602-612 https://dx.doi.org/10.1177/2168479013495685 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 47 2013 5 09 602-612 |
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10.1177/2168479013495685 doi (DE-627)SPR03892319X (SPR)2168479013495685-e DE-627 ger DE-627 rakwb eng Marchenko, Olga verfasserin aut Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Drug Information Association, Inc 2013 Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. innovative designs (dpeaa)DE-He213 program optimization (dpeaa)DE-He213 eNPV (dpeaa)DE-He213 program success (dpeaa)DE-He213 adaptive design (dpeaa)DE-He213 Miller, Joel aut Parke, Tom aut Perevozskaya, Inna aut Qian, Jiang aut Wang, Yanping aut Enthalten in Therapeutic innovation & regulatory science [New York] : Springer Nature, 2013 47(2013), 5 vom: Sept., Seite 602-612 (DE-627)739896415 (DE-600)2708397-4 2168-4804 nnns volume:47 year:2013 number:5 month:09 pages:602-612 https://dx.doi.org/10.1177/2168479013495685 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 47 2013 5 09 602-612 |
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Enthalten in Therapeutic innovation & regulatory science 47(2013), 5 vom: Sept., Seite 602-612 volume:47 year:2013 number:5 month:09 pages:602-612 |
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Marchenko, Olga @@aut@@ Miller, Joel @@aut@@ Parke, Tom @@aut@@ Perevozskaya, Inna @@aut@@ Qian, Jiang @@aut@@ Wang, Yanping @@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">SPR03892319X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519124302.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1177/2168479013495685</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR03892319X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)2168479013495685-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">Marchenko, Olga</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</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 2013</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. 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Marchenko, Olga |
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Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations innovative designs (dpeaa)DE-He213 program optimization (dpeaa)DE-He213 eNPV (dpeaa)DE-He213 program success (dpeaa)DE-He213 adaptive design (dpeaa)DE-He213 |
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improving oncology clinical programs by use of innovative designs and comparing them via simulations |
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Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations |
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Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. © Drug Information Association, Inc 2013 |
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Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. © Drug Information Association, Inc 2013 |
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Abstract The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages—better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs’ performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA’s Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program’s success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy. © Drug Information Association, Inc 2013 |
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| score |
7.399846 |