A mixed approach for proving non-inferiority in clinical trials with binary endpoints
被引:21
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作者:
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机构:
Rousson, Valentin
[1
]
Seifert, Burkhardt
论文数: 0引用数: 0
h-index: 0
机构:
Univ Zurich, Inst Social & Prevent Med, Biostat Unit, CH-8001 Zurich, SwitzerlandUniv Lausanne, Inst Social & Prevent Med, Stat Unit, CH-1005 Lausanne, Switzerland
Seifert, Burkhardt
[2
]
机构:
[1] Univ Lausanne, Inst Social & Prevent Med, Stat Unit, CH-1005 Lausanne, Switzerland
[2] Univ Zurich, Inst Social & Prevent Med, Biostat Unit, CH-8001 Zurich, Switzerland
active control;
non-inferiofity margin;
odds-ratio;
difference of proportions;
sample size calculation;
test for non-inferiority;
D O I:
10.1002/bimj.200710410
中图分类号:
Q [生物科学];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
When a new treatment is compared to an established one in a randomized clinical trial, it is standard practice to statistically test for non-inferiority rather than for superiority. When the endpoint is binary, one usually compares two treatments using either an odds-ratio or a difference of proportions. In this paper, we propose a mixed approach which uses both concepts. One first defines the non-inferiority margin using an odds-ratio and one ultimately proves non-inferiority statistically using a difference of proportions. The mixed approach is shown to be more powerful than the conventional odds-ratio approach when the efficacy of the established treatment is known (with good precision) and high (e.g. with more than 56% of success). The gain of power achieved may lead in turn to a substantial reduction in the sample size needed to prove non-inferiority. The mixed approach can be generalized to ordinal endpoints.
机构:
Stanford Univ, Dept Hlth Res & Policy, Div Biostat, HRP Biostat, Stanford, CA 94305 USAStanford Univ, Dept Hlth Res & Policy, Div Biostat, HRP Biostat, Stanford, CA 94305 USA
Bloch, Daniel A.
Lai, Tze Leung
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机构:Stanford Univ, Dept Hlth Res & Policy, Div Biostat, HRP Biostat, Stanford, CA 94305 USA
Lai, Tze Leung
Su, Zheng
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机构:Stanford Univ, Dept Hlth Res & Policy, Div Biostat, HRP Biostat, Stanford, CA 94305 USA
Su, Zheng
Tubert-Bitter, Pascale
论文数: 0引用数: 0
h-index: 0
机构:Stanford Univ, Dept Hlth Res & Policy, Div Biostat, HRP Biostat, Stanford, CA 94305 USA
机构:
China Jiliang Univ, Coll Econ & Management, Hangzhou, Peoples R China
China Jiliang Univ, Coll Econ & Management, Hangzhou 310018, Peoples R ChinaChina Jiliang Univ, Coll Econ & Management, Hangzhou, Peoples R China
Xu, Wenfu
Hou, Yuli
论文数: 0引用数: 0
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机构:
Baidu Co, Dept Intelligent Med, Beijing, Peoples R ChinaChina Jiliang Univ, Coll Econ & Management, Hangzhou, Peoples R China
Hou, Yuli
Lu, Tong-Yu
论文数: 0引用数: 0
h-index: 0
机构:
China Jiliang Univ, Coll Econ & Management, Hangzhou, Peoples R ChinaChina Jiliang Univ, Coll Econ & Management, Hangzhou, Peoples R China