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Impact of sample size and data origin on the simulation-based analytical performance specification derivation.

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Summary

A minimum sample size of 5,000 results is sufficient for reliable simulation-driven analytical performance specifications (APS). These specifications are transferable across different populations, with some exceptions for specific analytes.

Keywords:
analytical performance specificationsdataoutcomesample sizesimulation

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Area of Science:

  • Clinical chemistry
  • Laboratory medicine
  • Analytical chemistry

Background:

  • Indirect clinical outcome-based analytical performance specifications (APS) offer a potential alternative to direct studies.
  • However, questions remain regarding data requirements and the transferability of these specifications.

Purpose of the Study:

  • To determine the minimum sample size required for simulation-driven APS.
  • To investigate the impact of data origin on the reliability and transferability of APS.

Main Methods:

  • Analysis of six laboratory measurands across four datasets (three Turkish hospitals, US NHANES 2017-2020).
  • Determination of APS for measurement uncertainty (MU) using the 'APS Calculator'.
  • Evaluation of sample size effects (n=500 to 50,000) and data-origin effects via statistical analysis.

Main Results:

  • A sample size of 5,000 met stability and precision criteria (MAPE 1.99%, RME 6.86%).
  • Sample sizes of 2,000 exceeded performance thresholds (MAPE 2.32%, RME 10.67%).
  • APS for MU demonstrated high consistency across datasets (Pearson r 0.977-0.994).

Conclusions:

  • A minimum sample size of 5,000 results is adequate for reliable simulation-driven APS determination.
  • APS for MU are largely transferable across populations when decision limits and agreement targets are constant.
  • Caution is advised for analytes influenced by population exposure and method selectivity.