{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T08:50:25Z","timestamp":1791881425510,"version":"4.3.4"},"reference-count":25,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cytometry Pt A"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Routinely used statistical hypothesis tests are often inadequate for longitudinal flow cytometry data, necessitating more sophisticated modeling approaches. Analyzing dynamic biomarker expression across hematopoietic maturation presents several analytical challenges, notably nonlinearity, serial correlation between subsequent stages, and varying total cell counts. This study addresses these issues by comparing traditional stage\u2010by\u2010stage hypothesis tests with two marginal modeling approaches: Generalized least squares (GLS) and a generalized linear model fitted with generalized estimating equations (GEE\u2010GLM). We analyzed cell fractions expressing the dynamic biomarkers Bcl\u20102 (anti\u2010apoptotic marker) and Ki\u201067 (proliferation marker), as well as the Bcl\u20102:Ki\u201067 ratio, across 20 stages of erythropoietic maturation in bone marrow aspirates from 25 patients with myelodysplastic syndromes (MDS), 25 patients with acute myeloid leukemia (AML), and 50 nonmalignant controls. The GLS modeling approach accounts for nonlinearity and correlation between subsequent maturation stages, while GEE\u2010GLM additionally accounts for varying total cell counts by assigning greater weight to observations with larger cell counts. Instead of repeated stage\u2010by\u2010stage testing, GLS and GEE\u2010GLM model the average maturation profile across different sub\u2010populations, enabling visualization and robust population\u2010level statistical inference. We demonstrate that variations in the total number of cells influence the estimated population mean profiles and their confidence intervals. This effect is particularly pronounced during early maturation stages, where low Ki\u201067+ cell counts can lead to substantially different estimates and conclusions when using the GEE\u2010GLM approach. Conversely, the GLS approach offers the flexibility to model the unbounded Bcl\u20102:Ki\u201067 ratio, revealing differences between AML and nonmalignant patients across all 20 stages. The computational workflow and scripts are provided to facilitate reproducibility and application to similar time\u2010dependent flow cytometry datasets.<\/jats:p>","DOI":"10.1002\/cyto.a.70061","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T03:16:24Z","timestamp":1787282184000},"page":"664-673","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Robust Computational Workflow Utilizing Generalized Least Squares (\n                    <scp>GLS<\/scp>\n                    ) and Generalized Estimating Equations (\n                    <scp>GEE<\/scp>\n                    \u2010\n                    <scp>GLM<\/scp>\n                    ) for Longitudinal Flow Cytometry Maturation Data Analysis"],"prefix":"10.1002","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6054-4568","authenticated-orcid":false,"given":"Sixuan Joanna","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Clinical Chemistry and Hematology Zuyderland Medical Center  Sittard\u2010Geleen the Netherlands"},{"name":"School of Nutrition and Translational Research in Metabolism (NUTRIM) Maastricht University Medical Center  Maastricht the Netherlands"},{"name":"Central Diagnostic Laboratory (CDL) Maastricht University Medical Center  Maastricht the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6011-7783","authenticated-orcid":false,"given":"Ruben","family":"Deneer","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry and Hematology Zuyderland Medical Center  Sittard\u2010Geleen the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1576-7832","authenticated-orcid":false,"given":"Tom","family":"Schoenmakers","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry and Hematology Zuyderland Medical Center  Sittard\u2010Geleen the Netherlands"},{"name":"School of Nutrition and Translational Research in Metabolism (NUTRIM) Maastricht University Medical Center  Maastricht the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4694-8484","authenticated-orcid":false,"given":"Stefan G. C.","family":"Mestrum","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry and Hematology Zuyderland Medical Center  Sittard\u2010Geleen the Netherlands"},{"name":"Department of Genetics and Cell Biology, GROW\u2010Research Institute for Oncology and Reproduction Maastricht University Medical Center  Maastricht the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0217-6045","authenticated-orcid":false,"given":"Yvonne M. C.","family":"Henskens","sequence":"additional","affiliation":[{"name":"Central Diagnostic Laboratory (CDL) Maastricht University Medical Center  Maastricht the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2902-2927","authenticated-orcid":false,"given":"Otto","family":"Bekers","sequence":"additional","affiliation":[{"name":"Central Diagnostic Laboratory (CDL) Maastricht University Medical Center  Maastricht the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5136-2955","authenticated-orcid":false,"given":"Anton H. N.","family":"Hopman","sequence":"additional","affiliation":[{"name":"Department of Genetics and Cell Biology, GROW\u2010Research Institute for Oncology and Reproduction Maastricht University Medical Center  Maastricht the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1324-5843","authenticated-orcid":false,"given":"Frans C. S.","family":"Ramaekers","sequence":"additional","affiliation":[{"name":"Department of Genetics and Cell Biology, GROW\u2010Research Institute for Oncology and Reproduction Maastricht University Medical Center  Maastricht the Netherlands"},{"name":"Nordic\u2010MUbio, Part of Vector Laboratories Inc.  Susteren the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5186-5600","authenticated-orcid":false,"given":"Mathie P. G.","family":"Leers","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry and Hematology Zuyderland Medical Center  Sittard\u2010Geleen the Netherlands"},{"name":"Department of Environmental Sciences, Faculty of Science Open Universiteit  Heerlen the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.20008"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2012.122"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.20608"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2012.120"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3324\/haematol.2016.147843"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2352-3026(24)00251-5"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.3324\/haematol.2009.008532"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.leukres.2007.08.022"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.leukres.2024.107520"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.22231"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.21946"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1961.10482090"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_2_11_15_1","first-page":"21","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box G. 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