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|Title: ||Transferring Cut-off Values between Assays for Cerebrospinal Fluid Alzheimer's Disease Biomarkers|
|Authors: ||Garcia Barrado, Leandro|
Vanderstichele, Hugo M. J.
|Issue Date: ||2015|
|Publisher: ||IOS PRESS|
|Citation: ||JOURNAL OF ALZHEIMERS DISEASE, 49 (1), p. 187-199|
|Abstract: ||Current technologies quantifying cerebrospinal fluid biomarkers to identify subjects with Alzheimer's disease pathology report different concentrations in function of technology and suffer from between-laboratory variability. Hence, lab- and technology-specific cut-off values are required. It is common practice to establish cut-off values on small datasets and, in the absence of well-characterized samples, to transfer the cut-offs to another assay format using 'side-by-side' testing of samples with both assays. We evaluated the uncertainty in cut-off estimation and the performance of two methods of cut-off transfer by using two clinical datasets and simulated data. The cut-off for the new assay was transferred by applying the commonly-used linear regression approach and a new Bayesian method, which consists of using prior information about the current assay for estimation of the biomarker's distributions for the new assay. Simulations show that cut-offs established with current sample sizes are insufficiently precise and also show the effect of increasing sample sizes on the cut-offs' precision. The Bayesian method results in unbiased and less variable cut-offs with substantially narrower 95% confidence intervals compared to the linear-regression transfer. For the BIODEM datasets, the transferred cut-offs for INNO-BIA A beta(1-42) are 167.5 pg/mL (95% credible interval [156.1, 178.01 and 172.8 pg/mL (95% CI [147.6, 179.6]) with Bayesian and linear regression methods, respectively. For the EUROIMMUN assay, the estimated cut-offs are 402.8 pg/mL (95% credible interval [348.0, 473.9]) and 364.4 pg/mL (95% CI [269.7, 426.8]). Sample sizes and statistical methods used to establish and transfer cut-off values have to be carefully considered to guarantee optimal diagnostic performance of biomarkers.|
|Notes: ||[Barrado, Leandro Garcia; Burzykowski, Tomasz] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Diepenbeek, Belgium. [Coart, Els; Burzykowski, Tomasz] Int Inst Drug Dev, B-1340 Louvain, Belgium. [Vanderstichele, Hugo M. J.] ADx NeuroSci, Ghent, Belgium.
|ISI #: ||000364409100020|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2016|
|Appears in Collections: ||Research publications|
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