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We present a model of biomarker validation developed in our laboratory, the results of the validation study, and the impact of the estimation of the variance components on the design of future molecular epidemiologic studies. Four different biomarkers of exposure are illustrated: DNA-protein cross-link (DNA-PC), DNA-amino acid cross link (DNA-AA), metallothionein gene expression (MT), and autoantibodies to oxidized DNA bases (DNAox). The general scheme for the validation experiments involves n subjects measured on k occasions, with j replicate samples analyzed on each occasion. Multiple subjects, occasions, and replicates provide information on intersubject, intrasubject, and analytical measurement variability, respectively. The analysis of variance showed a significant effect of batch variability for DNA-PC and MT gene expression, whereas DNAox showed a significant between-subject variability. Among the amino acids tested, cysteine and methionine showed a significant contribution of both batch and between-subject variability, threonine showed between-subject variability only, and tyrosine showed between-batch and between-subject variability. The total variance estimated through the experiment was used to calculate the minimum sample size required for a future epidemiologic study including the same biomarkers used for the reliability study. Such validation studies can detect the various components of variability of a biomarker and indicate needed improvements of the assay, along with possible use in field studies.

Application of reliability models to studies of biomarker validation.