Improvement of Statistical Power to Detect Publication Bias in Meta-analysis Using the Clinical Trial Registration System
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概要
- 論文の詳細を見る
As clinical trials with “positive” results are more likely to be published, a meta-analysis of only published trials may be biased toward positive results (referred to as “publication bias”). A number of statistical tests have been proposed to detect publication bias. However, they have undesirable properties, particularly, the inflation of type I error and low power. A primordial countermeasure has been launched. In September 2004, the International Committee of Medical Journal Editors announced that they would no longer publish trials that were not registered in a public registry in advance. They embraced the WHO trial registration set consisting of 20 items including target sample size, which is related to the publication of results. The aim of this paper is to propose a new approach with a higher statistical power for detecting publication bias by using information on the sample sizes of all trials, including unpublished trials from the registry. We compared the proposed method to commonly used methods via simulations. The proposed method was found to have a higher power than the other methods in many situations. It will be useful for detecting publication bias because clinical trial registration will be more widespread in the near future.
- 日本計量生物学会の論文
- 2011-07-31
著者
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Hamada Chikuma
Department Of Management Of Science Faculty Of Engineering Tokyo University Of Science
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Matsuoka Nobushige
Pfizer Global R & D Tokyo Laboratories Statistical And Clinical Programming Pfizer Japan Inc.
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MATSUOKA Nobushige
Department of Management Science, Graduate School of Engineering, Tokyo University of Science
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HORIO Hiroshi
Department of Management Science, Graduate School of Engineering, Tokyo University of Science
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Matsuoka Nobushige
Department Of Management Science Graduate School Of Engineering Tokyo University Of Science
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Horio Hiroshi
Department Of Management Science Graduate School Of Engineering Tokyo University Of Science
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Hamada Chikuma
Department of Management Science, Faculty of Engineering, Tokyo University of Science
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