title: Assessing assay agreement estimation for multiple left-censored data: a multiple imputation approach.
abstract: Agreement between two assays is usually based on the concordance correlation coefficient (CCC), estimated from the means, standard deviations, and correlation coefficient of these assays. However, such data will often suffer from left-censoring because of lower limits of detection of these assays. To handle such data, we propose to extend a multiple imputation approach by chained equations (MICE) developed in a close setting of one left-censored assay. The performance of this two-step approach is compared with that of a previously published maximum likelihood estimation through a simulation study. Results show close estimates of the CCC by both methods, although the coverage is improved by our MICE proposal. An application to cytomegalovirus quantification data is provided.
authors.affiliation: SBIM, Hôpital Saint-Louis, APHP, Paris, France; Université Paris Diderot, Paris 7, SPC, Paris, France; INSERM, UMR_S 1136, Institut Pierre Louis d'Épidémiologie et de Santé Publique, F-75013, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d'Épidémiologie et de Santé Publique, F-75013, Paris, France.
authors.forename: Nathanael
authors.initials: N
authors.lastname: Lapidus
authors.forename: Sylvie
authors.initials: S
authors.lastname: Chevret
authors.forename: Matthieu
authors.initials: M
authors.lastname: Resche-Rigon
mesh_terms: D001681:Biological Assay / Q000592:standards*; D003198:Computer Simulation; D003587:Cytomegalovirus; D006801:Humans; D016013:Likelihood Functions; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: agreement; censoring; missing data; multiple imputation: chained equations