Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates

• 成果类型: Article
• 署名作者: Yi, Grace Y.; Ma, Yanyuan; Spiegelman, Donna; Carroll, Raymond J.
• 署名单位: University of Waterloo; Texas A&M University System; Texas A&M University College Station; Harvard University; Harvard T.H. Chan School of Public Health; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.922777
• 发表日期: 2015
• 页码: 681-696
• 关键词: maximum-likelihood-estimation; proportional hazards model; logistic-regression; semiparametric estimators; confidence-intervals; RISK; inference

摘要

Covariate measurement imprecision or errors arise frequently in many areas. It is well known that ignoring such errors can substantially degrade the quality of inference or even yield erroneous results. Although in practice both covariates subject to measurement error and covariates subject to misclassification can occur, research attention in the literature has mainly focused on addressing either one of these problems separately. To fill this gap, we develop estimation and inference methods that accommodate both characteristics simultaneously. Specifically, we consider measurement error and misclassification in generalized linear models under the scenario that an external validation study is available, and systematically develop a number of effective functional and structural methods. Our methods can be applied to different situations to meet various objectives.