Doubly misspecified models

作者：Lin N X^*; Shi J Q; Henderson R

来源：Biometrika, 2012, 99(2): 285-298.

DOI：10.1093/biomet/asr085

• Bias
• Confounding
• Frailty
• Measurement error
• Missing data
• Model uncertainty
• Nonlinear regression
• Sensitivity

Estimation bias arising from local model uncertainty and incomplete data has been studied by Copas %26 Eguchi (2005) under the assumption of a correctly specified marginal model. We extend the approach to allow additional local uncertainty in the assumed marginal model, arguing that this is almost unavoidable for nonlinear problems. We present a general bias analysis and sensitivity procedure for such doubly misspecified models and illustrate the breadth of application through three examples: logistic regression with a missing confounder, measurement error for binary responses and survival analysis with frailty. We show that a double-the-variance rule is not conservative under double misspecification. The ideas are brought together in a meta-analysis of studies of rehabilitation rates for juvenile offenders.

• 出版日期2012-6