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Table 1 Overall coefficients of variation (CVs), parameters of variance-component models decomposing total variability into between-cluster and within-cluster variability, and corresponding intraclass correlation coefficients (ICCs)

From: Interlaboratory proficiency processing scheme in CSF aliquoting: implementation and assessment based on biomarkers of Alzheimer’s disease

Biomarker Intracenter schemea Intercenter schemeb
CV (%)c \( \frac{\sqrt{\psi }}{\mu } \) (%) \( \frac{\sqrt{\theta }}{\mu } \) (%) ICC CV (%)c \( \frac{\sqrt{\psi }}{\mu } \) (%) \( \frac{\sqrt{\theta }}{\mu } \) (%) ICC
Aβ1–42 12 < 0.1 12 < 0.01 31 28 10 0.89
pTau181 3.2 3 0.8 0.93 8 2.5 7 0.11 (0.88)d
Albumin 4 < 0.1 4 < 0.01 10 2 9 0.05 (0.92)d
  1. μ represents overall average concentration of a given biomarker in a given scheme
  2. amyloid beta, PPS proficiency processing sample, SA secondary sample
  3. aIn the intracenter scheme, between-cluster (random intercept) variability (ψ) was the variability of the results obtained from 10 PPSs, and within-cluster (residual) variability (θ) was the variability of the results obtained in two SAs prepared from each PPS
  4. bIn the interlaboratory scheme, between-cluster (random intercept) variability (ψ) was the variability of the results obtained from 10 PPSs sent to the participating laboratories, and within-cluster (residual) variability (θ) was the variability of the results obtained in two SAs prepared in each laboratory from the PPS
  5. cUnadjusted total coefficient of variation of the results of the measurements of 20 SAs treated as 20 independent samples, irrespective of their origin from the PPSs
  6. dICCs after exclusion of the two centers (numbers 7 and 8) with apparent failure in their standardized operating procedures