A NOTE ON THE CORRECTED SCORE FUNCTION ADJUSTING FOR MISCLASSIFICATION

• Akazawa Kouhei

  Department of Medical Informatics, Faculty of Medicine, Kyushu University

• Kinukawa Naoko

  Department of Medical Informatics, Faculty of Medicine, Kyushu University

• Nakamura Tsuyoshi

  Faculty of Environmental Studies, Nagasaki University

抄録

This paper deals with a regression model with covariates subject to misclassification. Simply ignoring the misclassification generally yields bias in parameter estimation. A corrected score function (Nakamura, 1990) is developed to obtain asymptotically unbiased estimates adjusting for the misclassification. It is proved that a corrected score function always exists for a misclassification model whose likelihood consists of independent contributions. This existence theorem for misclassification model is conspicuous since corrected score functions do not always exist for measurement error models with continuous covariates. For instance, a corrected score function does not exist for a logistic model with continuous covariates subject to normal random error (Stefanski, 1989). For illustration, the correction method is applied to generalized linear models and a simulation study based on the logistic regression model is performed. The misclassification are assumed to be nondifferential, which is normally assumed with prospective studies.

収録刊行物

• ＪＯＵＲＮＡＬ　ＯＦ　ＴＨＥ　ＪＡＰＡＮ　ＳＴＡＴＩＳＴＩＣＡＬ　ＳＯＣＩＥＴＹ

  ＪＯＵＲＮＡＬ　ＯＦ　ＴＨＥ　ＪＡＰＡＮ　ＳＴＡＴＩＳＴＩＣＡＬ　ＳＯＣＩＥＴＹ 28 (1), 115-123, 1998

  日本統計学会

キーワード

• misclassification
• measurement error
• generalized linear model
• corrected likelihood
• corrected score

詳細情報

• CRID: 1390001205285969536
• NII論文ID: 130003582686
• DOI: 10.14490/jjss1995.28.115
• ISSN: 13486365
• ISSN: 18822754
• MRID: 1650383
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可