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

Objectives Indirect clinical outcome-based analytical performance specifications (APS) may offer a feasible alternative to direct studies but raise questions regarding data requirements and transferability. This study aimed to determine the minimum sample size for simulation-driven APS and to investigate the impact of data origin. Methods We analysed data for six laboratory measurands from four distinct datasets: three Turkish hospitals (Istanbul, Ankara, Van) and the US National Health and Nutrition Examination Survey (NHANES) 2017–2020 dataset. Using the ‘APS Calculator’, we determined APS for measurement uncertainty (MU). The effect of sample size was evaluated by subsampling the Istanbul dataset (n=50,000 to n=500) and calculating the Mean Absolute Percentage Error (MAPE) relative to the baseline (n=50,000) (acceptable <2 %). Precision of APS estimate was evaluated using nonparametric 95 % confidence intervals from simulation results and summarized as relative margin of error (RME; acceptable <10 %). Data-origin effects were assessed by Pearson correlation analysis of APS for MU across the four datasets. Results A sample size of n=5,000 met stability and precision criteria (MAPE 1.99 %; mean RME 6.86 %), whereas n=2,000 exceeded thresholds (MAPE 2.32 %; mean RME 10.67 %). APS for MU were highly consistent across datasets (Pearson r 0.977–0.994), with the main divergence observed for total folate in NHANES. Conclusions A minimum sample size of 5,000 results appears sufficient for a reliable simulation-driven APS determination. APS were largely transferable across populations when decision limits and agreement targets were constant, with caution for analytes influenced by population exposure and method selectivity.