Multivariate logistic regression with incomplete covariate and auxiliary information

JOURNAL OF MULTIVARIATE ANALYSIS

卷:101

Article

Sinha, Sanjoy K.¹  Laird, Nan M.²  Fitzmaurice, Garrett M.³ 

[1] Carleton Univ, Sch Math & Stat, Ottawa, ON K1S 5B6, Canada

[2] Harvard Univ, Sch Publ Hlth, Boston, MA 02115 USA

[3] Harvard Univ, Sch Med, Boston, MA USA

关键词: Asymptotic relative efficiency;    Auxiliary information;    Incomplete data;    Logistic regression model;    Missing covariates;    Multiple outcomes;   

DOI  :  10.1016/j.jmva.2010.06.010

来源: Elsevier

【 摘 要 】

In this article, we propose and explore a multivariate logistic regression model for analyzing multiple binary outcomes with incomplete covariate data where auxiliary information is available. The auxiliary data are extraneous to the regression model of interest but predictive of the covariate with missing data. Horton and Laird [N.J. Horton, N.M. Laird, Maximum likelihood analysis of logistic regression models with incomplete covariate data and auxiliary information, Biometrics 57 (2001) 34-42] describe how the auxiliary information can be incorporated into a regression model for a single binary outcome with missing covariates, and hence the efficiency of the regression estimators can be improved. We consider extending the method of [9] to the case of a multivariate logistic regression model for multiple correlated outcomes, and with missing covariates and completely observed auxiliary information. We demonstrate that in the case of moderate to strong associations among the multiple outcomes, one can achieve considerable gains in efficiency from estimators in a multivariate model as compared to the marginal estimators of the same parameters. (c) 2010 Elsevier Inc. All rights reserved.

【 授权许可】

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