Semiparametric Double Balancing Score Estimation for Incomplete Data With Ignorable Missingness

• 成果类型: Article
• 署名作者: Hu, Zonghui; Follmann, Dean A.; Qin, Jing
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.656009
• 发表日期: 2012
• 页码: 247-257
• 关键词: propensity score; regression; Consistency

摘要

When estimating the marginal mean response with missing observations, a critical issue is robustness to model misspecification. In this article, we propose a semiparametric estimation method with extended double robustness that attains the optimal efficiency under less stringent requirement for model specifications than the doubly robust estimators. In this semiparametric estimation, covariate information is collapsed into a two-dimensional score S. with one dimension for (i) the pattern of missingness and the other for (ii) the pattern of response, both estimated from some working parametric models. The mean response E(Y) is then estimated by the sample mean of E(Y vertical bar S), which is estimated via nonparametric regression. The semiparametric estimator is consistent if either the core of (i) or the core of (ii) is captured by S, and attains the optimal efficiency if both are captured by S. As the cores can be obtained without correctly specifying the full parametric models for (i) or (ii), the proposed estimator can be more robust than other doubly robust estimators. As S contains the propensity score as one component, the proposed estimator avoids the use and the shortcomings of inverse propensity weighting. This semiparametric estimator is most appealing for high-dimensional covariates, where fully correct model specification is challenging and nonparametric estimation is not feasible due to the problem of dimensionality. Numerical performance is investigated by simulation studies.