Transferring cut-off values between assays for cerebrospinal fluid Alzheimer's disease biomarkers.
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 Aβ1-42 are 167.5 pg/mL (95% credible interval [156.1, 178.0] 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.
Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.
Leandro
L
García Barrado
International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.
Els
E
Coart
ADx NeuroSciences, Gent, Belgium.
Hugo M J
HMJ
Vanderstichele
Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.|International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.
Tomasz
T
Burzykowski
D000544:Alzheimer Disease / Q000175:diagnosis*; D016229:Amyloid beta-Peptides / Q000134:cerebrospinal fluid*; D001499:Bayes Theorem; D015415:Biomarkers / Q000134:cerebrospinal fluid; D060825:Cognitive Dysfunction / Q000175:diagnosis*; D066264:Datasets as Topic; D006801:Humans; D016014:Linear Models; D015999:Multivariate Analysis; D010446:Peptide Fragments / Q000134:cerebrospinal fluid*; D016875:tau Proteins / Q000134:cerebrospinal fluid*
D016229:Amyloid beta-Peptides; D015415:Biomarkers; D010446:Peptide Fragments; C075222:amyloid beta-protein (1-42); D016875:tau Proteins
Alzheimer’s disease; Bayesian method; biomarker cut-off value; diagnostic accuracy