Toggle Main Menu Toggle Search

Open Access padlockePrints

Doubly misspecified models

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


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


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


DOI: 10.1093/biomet/asr085


Altmetrics provided by Altmetric