-
- Sakamoto Kimihiko
- Department of Biostatistics / Epidemiology and Preventive Health Sciences, School of Health Sciences and Nursing, University of Tokyo
-
- Matsuyama Yutaka
- Department of Biostatistics / Epidemiology and Preventive Health Sciences, School of Health Sciences and Nursing, University of Tokyo
-
- Ohashi Yasuo
- Department of Biostatistics / Epidemiology and Preventive Health Sciences, School of Health Sciences and Nursing, University of Tokyo
この論文をさがす
抄録
Due to the selection process in academic publication, all meta-analysis of published literature is more or less affected by the so-called publication bias and tends to overestimate the effect of interest. Statistically, publication bias in meta-analysis is a selection bias which results from a non-random sampling from the population of unpublished studies. Several authors proposed methods of modelling publication bias using a selection model approach, which considers a joint modelling of the weight function representing the publication probability of each study and a regression of the outcome of interest. Copas (1999) showed that in this approach some of the model parameters are not estimable and a sensitivity analysis should be conducted. In implementing the Copas's sensitivity analysis of publication bias, a practical difficulty arises in determining the range of sensitivity parameters appropriately. We propose in this article a Bayesian hierarchical model which extends Copas's selectivity model and incorporates the experts' opinions as a prior distribution of sensitivity parameters. We illustrate this approach with an example of the passive smoking and lung cancer meta-analysis.
収録刊行物
-
- 計量生物学
-
計量生物学 27 (2), 109-119, 2006
日本計量生物学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390282679347568384
-
- NII論文ID
- 10018385914
-
- NII書誌ID
- AA11591618
-
- ISSN
- 21856494
- 09184430
-
- NDL書誌ID
- 8596766
-
- 本文言語コード
- en
-
- データソース種別
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可