{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T13:47:56Z","timestamp":1777816076360,"version":"3.51.4"},"reference-count":17,"publisher":"Walter de Gruyter GmbH","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_cclm-2026-0112_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0112_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u20132020 dataset. Using the \u2018APS Calculator\u2019, 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 &lt;2\u202f%). Precision of APS estimate was evaluated using nonparametric 95\u202f% confidence intervals from simulation results and summarized as relative margin of error (RME; acceptable &lt;10\u202f%). Data-origin effects were assessed by Pearson correlation analysis of APS for MU across the four datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0112_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A sample size of n=5,000 met stability and precision criteria (MAPE 1.99\u202f%; mean RME 6.86\u202f%), whereas n=2,000 exceeded thresholds (MAPE 2.32\u202f%; mean RME 10.67\u202f%). APS for MU were highly consistent across datasets (Pearson r 0.977\u20130.994), with the main divergence observed for total folate in NHANES.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0112_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/cclm-2026-0112","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T17:00:47Z","timestamp":1773939647000},"source":"Crossref","is-referenced-by-count":0,"title":["Impact of sample size and data origin on the simulation-based analytical performance specification derivation"],"prefix":"10.1515","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5321-9354","authenticated-orcid":false,"given":"Hikmet Can","family":"\u00c7ubuk\u00e7u","sequence":"first","affiliation":[{"name":"Department of Medical Biochemistry , Sincan Training and Research Hospital , Ankara , T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9202-4944","authenticated-orcid":false,"given":"Hamit Hakan","family":"Alp","sequence":"additional","affiliation":[{"name":"Department of Medical Biochemistry, Faculty of Medicine , Van Y\u00fcz\u00fcnc\u00fc Y\u0131l University , Van , T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4614-5235","authenticated-orcid":false,"given":"O\u011fuzhan","family":"Zengi","sequence":"additional","affiliation":[{"name":"Department of Medical Biochemistry , Health Science University, Istanbul Basaksehir Cam and Sakura City Hospital , Istanbul , T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0203-981X","authenticated-orcid":false,"given":"Merve Sibel","family":"G\u00fcng\u00f6ren","sequence":"additional","affiliation":[{"name":"Department of Medical Biochemistry , Mamak State Hospital , Ankara , T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1771-669X","authenticated-orcid":false,"given":"Marc","family":"Thelen","sequence":"additional","affiliation":[{"name":"SKML , Nijmegen , The Netherlands"},{"name":"Department of Laboratory Medicine , Radboud University Medical Center , Nijmegen , The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7147-3433","authenticated-orcid":false,"given":"Mauro","family":"Panteghini","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun , Torun , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"2026042921153040756_j_cclm-2026-0112_ref_001","doi-asserted-by":"crossref","unstructured":"Panteghini, M, Krintus, M. 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Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM strategic conference. Clin Chem Lab Med 2017;55:189\u201394. https:\/\/doi.org\/10.1515\/cclm-2016-0091.","DOI":"10.1515\/cclm-2016-0091"},{"key":"2026042921153040756_j_cclm-2026-0112_ref_004","doi-asserted-by":"crossref","unstructured":"Horvath, AR, Bossuyt, PM, Sandberg, S, John, AS, Monaghan, PJ, Verhagen-Kamerbeek, WD, et al.. Setting analytical performance specifications based on outcome studies\u00a0\u2013\u00a0is it possible? Clin Chem Lab Med 2015;53:841\u20138. https:\/\/doi.org\/10.1515\/cclm-2015-0214.","DOI":"10.1515\/cclm-2015-0214"},{"key":"2026042921153040756_j_cclm-2026-0112_ref_005","doi-asserted-by":"crossref","unstructured":"\u00c7ubuk\u00e7u, HC, Vanstapel, F, Thelen, M, van Schrojenstein Lantman, M, Bernabeu-Andreu, FA, Me\u0161ko Brguljan, P, et al.. APS calculator: a data-driven tool for setting outcome-based analytical performance specifications for measurement uncertainty using specific clinical requirements and population data. Clin Chem Lab Med 2024;62:597\u2013607. https:\/\/doi.org\/10.1515\/cclm-2023-0740.","DOI":"10.1515\/cclm-2023-0740"},{"key":"2026042921153040756_j_cclm-2026-0112_ref_006","doi-asserted-by":"crossref","unstructured":"\u00c7ubuk\u00e7u, HC. Computer simulation approaches to evaluate the interaction between analytical performance characteristics and clinical (mis)classification: a complementary tool for setting indirect outcome-based analytical performance specifications. Clin Chem Lab Med 2025;63:1292\u2013300. https:\/\/doi.org\/10.1515\/cclm-2024-1195.","DOI":"10.1515\/cclm-2024-1195"},{"key":"2026042921153040756_j_cclm-2026-0112_ref_007","doi-asserted-by":"crossref","unstructured":"Panteghini, M. Moving to practice with the application of milan model 1b-based analytical performance specifications. 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Geneva: World Health Organization; 2015."},{"key":"2026042921153040756_j_cclm-2026-0112_ref_011","doi-asserted-by":"crossref","unstructured":"Giustina, A, Adler, RA, Binkley, N, Bouillon, R, Ebeling, PR, Lazaretti-Castro, M, et al.. Controversies in vitamin D: summary statement from an international conference. J\u00a0Clin Endocrinol Metab 2019;104:234\u201340. https:\/\/doi.org\/10.1210\/jc.2018-01414.","DOI":"10.1210\/jc.2018-01414"},{"key":"2026042921153040756_j_cclm-2026-0112_ref_012","doi-asserted-by":"crossref","unstructured":"Standards of Medical Care in Diabetes-2022 Abridged for Primary Care Providers. Standards of medical care in Diabetes\u20132022 abridged for primary care providers. Clin Diabetes 2022;40:10\u201338. https:\/\/doi.org\/10.2337\/cd22-as01.","DOI":"10.2337\/cd22-as01"},{"key":"2026042921153040756_j_cclm-2026-0112_ref_013","doi-asserted-by":"crossref","unstructured":"Boyd, JC, Bruns, DE. Quality specifications for glucose meters: assessment by simulation modeling of errors in insulin dose. Clin Chem 2001;47:209\u201314. https:\/\/doi.org\/10.1093\/clinchem\/47.2.209.","DOI":"10.1093\/clinchem\/47.2.209"},{"key":"2026042921153040756_j_cclm-2026-0112_ref_014","doi-asserted-by":"crossref","unstructured":"Ferraro, S, Panzeri, A, Panteghini, M. Tackling serum folate test in European countries within the health technology assessment paradigm: request appropriateness, assays and health outcomes. Clin Chem Lab Med 2017;55:1262\u201375. https:\/\/doi.org\/10.1515\/cclm-2016-0804.","DOI":"10.1515\/cclm-2016-0804"},{"key":"2026042921153040756_j_cclm-2026-0112_ref_015","doi-asserted-by":"crossref","unstructured":"Braga, F, Frusciante, E, Ferraro, S, Panteghini, M. Trueness evaluation and verification of inter-assay agreement of serum folate measuring systems. 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