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A simple methodology to analyze inter-laboratory data: A simulation study

Clinica Chimica Acta
Publication Date
DOI: 10.1016/j.cca.2009.09.027
  • Inter-Laboratory Experiments
  • Bias
  • Precision
  • Repeatability
  • Reproducibility
  • Homogeneity Of Variance
  • Chemistry
  • Computer Science


Abstract Background Inter-laboratory experiments are conducted to assess how accurately and reproducibly various laboratories using different methods, instruments, analysts, and/or sample preparation procedures can perform measurements, in this case, the concentration of a chemical. In this work a 3-step methodology is proposed to analyze inter-laboratory experiments. Methods A simulation study based on 500 simulations was conducted for a cluster of 12 laboratories with different population means and SDs. The sample sizes varied from 10 to 50. Laboratories with too high a variance or too high a mean were recursively identified and removed by analysis of variance techniques. Outliers were identified and removed by a recursive algorithm. The remaining data were used to compute consensus mean, SD, repeatability, reproducibility, and CV for repeatability and reproducibility. Results Two laboratories with too high a variance were always identified for removal when the sample size was ≥ 20. Two laboratories with too high a mean were almost always identified irrespective of sample size. The average observed percent bias was never > ± 3.2% irrespective of the sample size. The average percent imprecision was also within ± 10.4% for all laboratories. The average CV was close to what was expected. Conclusions With an optimal sample size of 20, the 3-step methodology presented here will adequately identify laboratories with variances or means that are too high or too low.

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