title: Bayesian analysis of censored response data in family-based genetic association studies.
abstract: Biomarkers are subject to censoring whenever some measurements are not quantifiable given a laboratory detection limit. Methods for handling censoring have received less attention in genetic epidemiology, and censored data are still often replaced with a fixed value. We compared different strategies for handling a left-censored continuous biomarker in a family-based study, where the biomarker is tested for association with a genetic variant, S, adjusting for a covariate, X. Allowing different correlations between X and S, we compared simple substitution of censored observations with the detection limit followed by a linear mixed effect model (LMM), Bayesian model with noninformative priors, Tobit model with robust standard errors, the multiple imputation (MI) with and without S in the imputation followed by a LMM. Our comparison was based on real and simulated data in which 20% and 40% censoring were artificially induced. The complete data were also analyzed with a LMM. In the MICROS study, the Bayesian model gave results closer to those obtained with the complete data. In the simulations, simple substitution was always the most biased method, the Tobit approach gave the least biased estimates at all censoring levels and correlation values, the Bayesian model and both MI approaches gave slightly biased estimates but smaller root mean square errors. On the basis of these results the Bayesian approach is highly recommended for candidate gene studies; however, the computationally simpler Tobit and the MI without S are both good options for genome-wide studies.
authors.affiliation: Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), Bolzano/Bozen, Italy (affiliated to the University of Lübeck, Lübeck, Germany). fabiola.delgreco@eurac.edu.
authors.forename: Fabiola
authors.initials: F
authors.lastname: Del Greco M
authors.affiliation: Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), Bolzano/Bozen, Italy (affiliated to the University of Lübeck, Lübeck, Germany).
authors.forename: Cristian
authors.initials: C
authors.lastname: Pattaro
authors.affiliation: Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, London, United Kingdom.
authors.forename: Cosetta
authors.initials: C
authors.lastname: Minelli
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
authors.forename: John R
authors.initials: JR
authors.lastname: Thompson
mesh_terms: D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis; D003198:Computer Simulation; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D008957:Models, Genetic
chemical_list: D015415:Biomarkers
keywords: Bayesian methods; Genetic association studies; Left-censored data; Multiple imputation; Tobit model