A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable | Zendy

Marine Corbin | Zendy; Stephen Haslett | Zendy; Neil Pearce | Zendy; Milena Maule | Zendy; Sander Greenland | Zendy

Open Access

A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable

Author(s) -

Marine Corbin,

Stephen Haslett,

Neil Pearce,

Milena Maule,

Sander Greenland

Publication year - 2017

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyx027

Subject(s) - statistics , confidence interval , imputation (statistics) , bayesian probability , point estimation , odds ratio , observational error , covariate , econometrics , mathematics , computer science , missing data

Measurement error is an important source of bias in epidemiological studies. We illustrate three approaches to sensitivity analysis for the effect of measurement error: imputation of the 'true' exposure based on specifying the sensitivity and specificity of the measured exposure (SS); direct imputation (DI) using a regression model for the predictive values; and adjustment based on a fully Bayesian analysis.

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