Implementation of adaptive methods in early-phase clinical trials
被引:25
作者:
Petroni, Gina R.
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Univ Virginia, Dept Publ Hlth Sci, Div Translat Res & Appl Stat, Charlottesville, VA 22908 USAUniv Virginia, Dept Publ Hlth Sci, Div Translat Res & Appl Stat, Charlottesville, VA 22908 USA
Petroni, Gina R.
[1
]
Wages, Nolan A.
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机构:
Univ Virginia, Dept Publ Hlth Sci, Div Translat Res & Appl Stat, Charlottesville, VA 22908 USAUniv Virginia, Dept Publ Hlth Sci, Div Translat Res & Appl Stat, Charlottesville, VA 22908 USA
Wages, Nolan A.
[1
]
Paux, Gautier
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机构:
IRIS, Oncol Clin Biostat, F-92284 Suresnes, FranceUniv Virginia, Dept Publ Hlth Sci, Div Translat Res & Appl Stat, Charlottesville, VA 22908 USA
Paux, Gautier
[2
]
Dubois, Frederic
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IRIS, Oncol Clin Biostat, F-92284 Suresnes, FranceUniv Virginia, Dept Publ Hlth Sci, Div Translat Res & Appl Stat, Charlottesville, VA 22908 USA
Dubois, Frederic
[2
]
机构:
[1] Univ Virginia, Dept Publ Hlth Sci, Div Translat Res & Appl Stat, Charlottesville, VA 22908 USA
[2] IRIS, Oncol Clin Biostat, F-92284 Suresnes, France
There has been constant development of novel statistical methods in the design of early-phase clinical trials since the introduction of model-based designs, yet the traditional or modified 3+3 algorithmic design remains the most widely used approach in dose-finding studies. Research has shown the limitations of this traditional design compared with more innovative approaches yet the use of these model-based designs remains infrequent. This can be attributed to several causes including a poor understanding from clinicians and reviewers into how the designs work, and how best to evaluate the appropriateness of a proposed design. These barriers are likely to be enhanced in the coming years as the recent paradigm of drug development involves a shift to more complex dose-finding problems. This article reviews relevant information that should be included in clinical trial protocols to aid in the acceptance and approval of novel methods. We provide practical guidance for implementing these efficient designs with the aim of augmenting a broader transition from algorithmic to adaptive model-guided designs. In addition we highlight issues to consider in the actual implementation of a trial once approval is obtained. Copyright (C) 2016 John Wiley & Sons, Ltd.
机构:
Columbia Univ, Mailman Sch Publ Hlth, Dept Biostat, New York, NY 10032 USAColumbia Univ, Mailman Sch Publ Hlth, Dept Biostat, New York, NY 10032 USA
机构:
Mem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USAMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
Iasonos, Alexia
Wilton, Andrew S.
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Inst Clin Evaluat Sci, Toronto, ON, CanadaMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
Wilton, Andrew S.
Riedel, Elyn R.
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Mem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USAMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
Riedel, Elyn R.
Seshan, Venkatraman E.
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机构:
Columbia Univ, Mailman Sch Publ Hlth, Dept Biostat, New York, NY 10032 USAMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
Seshan, Venkatraman E.
Spriggs, David R.
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机构:
Mem Sloan Kettering Canc Ctr, Dept Med, Div Solid Tumor Oncol, New York, NY 10021 USAMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
机构:
Columbia Univ, Mailman Sch Publ Hlth, Dept Biostat, New York, NY 10032 USAColumbia Univ, Mailman Sch Publ Hlth, Dept Biostat, New York, NY 10032 USA
机构:
Mem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USAMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
Iasonos, Alexia
Wilton, Andrew S.
论文数: 0引用数: 0
h-index: 0
机构:
Inst Clin Evaluat Sci, Toronto, ON, CanadaMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
Wilton, Andrew S.
Riedel, Elyn R.
论文数: 0引用数: 0
h-index: 0
机构:
Mem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USAMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
Riedel, Elyn R.
Seshan, Venkatraman E.
论文数: 0引用数: 0
h-index: 0
机构:
Columbia Univ, Mailman Sch Publ Hlth, Dept Biostat, New York, NY 10032 USAMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA
Seshan, Venkatraman E.
Spriggs, David R.
论文数: 0引用数: 0
h-index: 0
机构:
Mem Sloan Kettering Canc Ctr, Dept Med, Div Solid Tumor Oncol, New York, NY 10021 USAMem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY 10021 USA