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Doubly misspecified models

Lookup NU author(s): Dr Nan Lin, Dr Jian Shi, Professor Robin Henderson

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Abstract

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.

Publication metadata

Author(s): Lin NX, Shi JQ, Henderson R

Publication type: Article

Publication status: Published

Journal: Biometrika

Year: 2012

Volume: 99

Issue: 2

Pages: 285-298

Print publication date: 26/02/2012

ISSN (print): 0006-3444

ISSN (electronic): 1464-3510

Publisher: Oxford University Press

URL: http://dx.doi.org/10.1093/biomet/asr085

DOI: 10.1093/biomet/asr085

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