Doubly misspecified models

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

Biometrika, 2012, vol. 99, issue 2, 285-298

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. Copyright 2012, Oxford University Press.

Date: 2012
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