{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T13:26:12Z","timestamp":1789565172104,"version":"build-2803163510"},"reference-count":27,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CPT Pharmacom &amp; Syst Pharma"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In population pharmacokinetic (PK) models, interindividual variability is explained by implementation of covariates in the model. The widely used forward stepwise selection method is sensitive to bias, which may lead to an incorrect inclusion of covariates. Alternatives, such as the full fixed effects model, reduce this bias but are dependent on the chosen implementation of each covariate. As the correct functional forms are unknown, this may still lead to an inaccurate selection of covariates. Machine learning (ML) techniques can potentially be used to learn the optimal functional forms for implementing covariates directly from data. A recent study suggested that using ML resulted in an improved selection of influential covariates. However, how do we select the appropriate functional form for including these covariates? In this work, we use SHapley Additive exPlanations (SHAP) to infer the relationship between covariates and PK parameters from ML models. As a case\u2010study, we use data from 119 patients with hemophilia A receiving clotting factor VIII concentrate peri\u2010operatively. We fit both a random forest and a XGBoost model to predict empirical Bayes estimated clearance and central volume from a base nonlinear mixed effects model. Next, we show that SHAP reveals covariate relationships which match previous findings. In addition, we can reveal subtle effects arising from combinations of covariates difficult to obtain using other methods of covariate analysis. We conclude that the proposed method can be used to extend ML\u2010based covariate selection, and holds potential as a complete full model alternative to classical covariate analyses.<\/jats:p>","DOI":"10.1002\/psp4.12828","type":"journal-article","created":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T02:38:33Z","timestamp":1656124713000},"page":"1100-1110","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Application of\n                    <scp>SHAP<\/scp>\n                    values for inferring the optimal functional form of covariates in pharmacokinetic modeling"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0719-0831","authenticated-orcid":false,"given":"Alexander","family":"Janssen","sequence":"first","affiliation":[{"name":"Department of Clinical Pharmacology, Hospital Pharmacy Amsterdam University Medical Center  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Hoogendoorn","sequence":"additional","affiliation":[{"name":"Quantitative Data Analytics Group, Department of Computer Science, Vrije Universiteit Amsterdam  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marjon H.","family":"Cnossen","sequence":"additional","affiliation":[{"name":"Department of Pediatric Hematology, Erasmus MC Sophia Children\u2019s Hospital Erasmus University Medical Center  Rotterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ron A. A.","family":"Math\u00f4t","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacology, Hospital Pharmacy Amsterdam University Medical Center  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the OPTI\u2010CLOT Study Group and SYMPHONY Consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2044-8317.1992.tb00992.x"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:JOPA.0000034404.86036.72"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2307\/2684336"},{"key":"e_1_2_9_5_1","unstructured":"GastonguayM(2011)Full covariate models as an alternative to methods relying on statistical significance for inferences about covariate effects: A review of methodology and 42 case studies. In: Annual meeting of the population approach group in Europe. Athens Greece."},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-021-09757-w"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.1724"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/cge.12890"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-8-25"},{"key":"e_1_2_9_12_1","unstructured":"LundbergS LeeS\u2010I(2017)A unified approach to interpreting model predictions. arXiv preprint arXiv:170507874"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"ChenT GuestrinC(2016)Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. pp.785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.3324\/haematol.2015.136275"},{"key":"e_1_2_9_16_1","doi-asserted-by":"crossref","unstructured":"KoshyM WeinerSJ MillerST et al. (1995)Surgery and anesthesia in sickle cell disease. Cooperative Study of Sickle Cell Diseases","DOI":"10.1182\/blood.V86.10.3676.bloodjournal86103676"},{"key":"e_1_2_9_17_1","unstructured":"BealSL SheinerLB BoeckmannAJ BauerRJ(Eds).NONMEM 7.4 users guides;1989\u20132018.https:\/\/nonmem.iconplc.com\/nonmem743\/guides"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2011-07-360594"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-019-09634-7"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.5334\/jors.151"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpdd.167"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/jth.13662"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.14670"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood.V69.6.1691.1691"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.critrevonc.2006.06.004"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1537-2995.2010.02604.x"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0037-1963(01)90104-0"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365-2516.1998.440646.x"}],"container-title":["CPT: Pharmacometrics &amp; Systems Pharmacology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/psp4.12828","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/psp4.12828","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/psp4.12828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T23:20:29Z","timestamp":1703892029000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/10.1002\/psp4.12828"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,24]]},"references-count":27,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.1002\/psp4.12828"],"URL":"https:\/\/doi.org\/10.1002\/psp4.12828","archive":["Portico"],"relation":{},"ISSN":["2163-8306","2163-8306"],"issn-type":[{"value":"2163-8306","type":"print"},{"value":"2163-8306","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,24]]},"assertion":[{"value":"2021-11-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-18","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}