Doubly misspecified models

N. X. Lin, J. Q. Shi, R. Henderson

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Estimation bias arising from local model uncertainty and incomplete data has been studied by Copas & 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.
Original languageEnglish
Pages (from-to)285-298
Number of pages14
Issue number2
Early online date26 Feb 2012
Publication statusPublished - 1 Jun 2012
Externally publishedYes


Dive into the research topics of 'Doubly misspecified models'. Together they form a unique fingerprint.

Cite this