Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials
Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hung, H. M. James [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© International Chinese Statistical Association (outside the US) 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Statistics in biosciences - New York, NY : Springer, 2009, 10(2017), 2 vom: 12. Juni, Seite 460-472 |
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| Übergeordnetes Werk: |
volume:10 ; year:2017 ; number:2 ; day:12 ; month:06 ; pages:460-472 |
| Links: |
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| DOI / URN: |
10.1007/s12561-017-9196-x |
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| Katalog-ID: |
SPR026590654 |
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| 100 | 1 | |a Hung, H. M. James |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials |
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| 520 | |a Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. | ||
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| 650 | 4 | |a Global treatment effect |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Inconsistency |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Random-effect |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Regional treatment effect |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Regional heterogeneity |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhang, Xiangmin |4 aut | |
| 700 | 1 | |a Wang, Sue-Jane |4 aut | |
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10.1007/s12561-017-9196-x doi (DE-627)SPR026590654 (SPR)s12561-017-9196-x-e DE-627 ger DE-627 rakwb eng Hung, H. M. James verfasserin aut Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association (outside the US) 2017 Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. Fixed-effect (dpeaa)DE-He213 Global treatment effect (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Random-effect (dpeaa)DE-He213 Regional treatment effect (dpeaa)DE-He213 Regional heterogeneity (dpeaa)DE-He213 Zhang, Xiangmin aut Wang, Sue-Jane aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 10(2017), 2 vom: 12. Juni, Seite 460-472 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:10 year:2017 number:2 day:12 month:06 pages:460-472 https://dx.doi.org/10.1007/s12561-017-9196-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 10 2017 2 12 06 460-472 |
| spelling |
10.1007/s12561-017-9196-x doi (DE-627)SPR026590654 (SPR)s12561-017-9196-x-e DE-627 ger DE-627 rakwb eng Hung, H. M. James verfasserin aut Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association (outside the US) 2017 Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. Fixed-effect (dpeaa)DE-He213 Global treatment effect (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Random-effect (dpeaa)DE-He213 Regional treatment effect (dpeaa)DE-He213 Regional heterogeneity (dpeaa)DE-He213 Zhang, Xiangmin aut Wang, Sue-Jane aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 10(2017), 2 vom: 12. Juni, Seite 460-472 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:10 year:2017 number:2 day:12 month:06 pages:460-472 https://dx.doi.org/10.1007/s12561-017-9196-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 10 2017 2 12 06 460-472 |
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10.1007/s12561-017-9196-x doi (DE-627)SPR026590654 (SPR)s12561-017-9196-x-e DE-627 ger DE-627 rakwb eng Hung, H. M. James verfasserin aut Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association (outside the US) 2017 Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. Fixed-effect (dpeaa)DE-He213 Global treatment effect (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Random-effect (dpeaa)DE-He213 Regional treatment effect (dpeaa)DE-He213 Regional heterogeneity (dpeaa)DE-He213 Zhang, Xiangmin aut Wang, Sue-Jane aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 10(2017), 2 vom: 12. Juni, Seite 460-472 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:10 year:2017 number:2 day:12 month:06 pages:460-472 https://dx.doi.org/10.1007/s12561-017-9196-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 10 2017 2 12 06 460-472 |
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10.1007/s12561-017-9196-x doi (DE-627)SPR026590654 (SPR)s12561-017-9196-x-e DE-627 ger DE-627 rakwb eng Hung, H. M. James verfasserin aut Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association (outside the US) 2017 Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. Fixed-effect (dpeaa)DE-He213 Global treatment effect (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Random-effect (dpeaa)DE-He213 Regional treatment effect (dpeaa)DE-He213 Regional heterogeneity (dpeaa)DE-He213 Zhang, Xiangmin aut Wang, Sue-Jane aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 10(2017), 2 vom: 12. Juni, Seite 460-472 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:10 year:2017 number:2 day:12 month:06 pages:460-472 https://dx.doi.org/10.1007/s12561-017-9196-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 10 2017 2 12 06 460-472 |
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10.1007/s12561-017-9196-x doi (DE-627)SPR026590654 (SPR)s12561-017-9196-x-e DE-627 ger DE-627 rakwb eng Hung, H. M. James verfasserin aut Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association (outside the US) 2017 Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. Fixed-effect (dpeaa)DE-He213 Global treatment effect (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Random-effect (dpeaa)DE-He213 Regional treatment effect (dpeaa)DE-He213 Regional heterogeneity (dpeaa)DE-He213 Zhang, Xiangmin aut Wang, Sue-Jane aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 10(2017), 2 vom: 12. Juni, Seite 460-472 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:10 year:2017 number:2 day:12 month:06 pages:460-472 https://dx.doi.org/10.1007/s12561-017-9196-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 10 2017 2 12 06 460-472 |
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Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. 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Hung, H. M. James |
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Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials Fixed-effect (dpeaa)DE-He213 Global treatment effect (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Random-effect (dpeaa)DE-He213 Regional treatment effect (dpeaa)DE-He213 Regional heterogeneity (dpeaa)DE-He213 |
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some recent advances on statistical approaches for planning multi-regional clinical trials |
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Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials |
| abstract |
Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. © International Chinese Statistical Association (outside the US) 2017 |
| abstractGer |
Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. © International Chinese Statistical Association (outside the US) 2017 |
| abstract_unstemmed |
Abstract Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced. © International Chinese Statistical Association (outside the US) 2017 |
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Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials |
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Zhang, Xiangmin Wang, Sue-Jane |
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10.1007/s12561-017-9196-x |
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| score |
7.4014626 |