000212517 001__ 212517 000212517 005__ 20240229154913.0 000212517 0247_ $$2doi$$a10.1177/23814683221145701 000212517 0247_ $$2pmid$$apmid:36698854 000212517 0247_ $$2pmc$$apmc:PMC9869210 000212517 037__ $$aDKFZ-2023-00202 000212517 041__ $$aEnglish 000212517 082__ $$a610 000212517 1001_ $$0P:(DE-He78)d2944f54ead34dbf6fb03e359225a1b9$$aCheng, Chih-Yuan$$b0$$eFirst author 000212517 245__ $$aModeling the Natural History and Screening Effects of Colorectal Cancer Using Both Adenoma and Serrated Neoplasia Pathways: The Development, Calibration, and Validation of a Discrete Event Simulation Model. 000212517 260__ $$aLondon$$bSage Publishing$$c2023 000212517 3367_ $$2DRIVER$$aarticle 000212517 3367_ $$2DataCite$$aOutput Types/Journal article 000212517 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1675070763_21995 000212517 3367_ $$2BibTeX$$aARTICLE 000212517 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000212517 3367_ $$00$$2EndNote$$aJournal Article 000212517 500__ $$a#EA:C100#LA:C100# 000212517 520__ $$aBackground. Existing colorectal cancer (CRC) screening models mostly focus on the adenoma pathway of CRC development, overlooking the serrated neoplasia pathway, which might result in overly optimistic screening predictions. In addition, Bayesian inference methods have not been widely used for model calibration. We aimed to develop a CRC screening model accounting for both pathways, calibrate it with approximate Bayesian computation (ABC) methods, and validate it with large CRC screening trials. Methods. A discrete event simulation (DES) of the CRC natural history (DECAS) was constructed using the adenoma and serrated pathways in R software. The model simulates CRC-related events in a specific birth cohort through various natural history states. Calibration took advantage of 74 prevalence data points from the German screening colonoscopy program of 5.2 million average-risk participants using an ABC method. CRC incidence outputs from DECAS were validated with the German national cancer registry data; screening effects were validated using 17-y data from the UK Flexible Sigmoidoscopy Screening sigmoidoscopy trial and a German screening colonoscopy cohort study. Results. The Bayesian calibration rendered 1,000 sets of posterior parameter samples. With the calibrated parameters, the observed age- and sex-specific CRC prevalences from the German registries were within the 95% DECAS-predicted intervals. Regarding screening effects, DECAS predicted a 41% (95% intervals 30%-51%) and 62% (95% intervals 55%-68%) reduction in 17-y cumulative CRC mortality for a single screening sigmoidoscopy and colonoscopy, respectively, falling within 95% confidence intervals reported in the 2 clinical studies used for validation. Conclusions. We presented DECAS, the first Bayesian-calibrated DES model for CRC natural history and screening, accounting for 2 CRC tumorigenesis pathways. The validated model can serve as a valid tool to evaluate the (cost-)effectiveness of CRC screening strategies.This article presents a new discrete event simulation model, DECAS, which models both adenoma-carcinoma and serrated neoplasia pathways for colorectal cancer (CRC) development and CRC screening effects.DECAS is calibrated based on a Bayesian inference method using the data from German screening colonoscopy program, which consists of more than 5 million first-time average-risk participants aged 55 years and older in 2003 to 2014.DECAS is flexible for evaluating various CRC screening strategies and can differentiate screening effects in different parts of the colon.DECAS is validated with large screening sigmoidoscopy and colonoscopy clinical study data and can be further used to evaluate the (cost-)effectiveness of German colorectal cancer screening strategies. 000212517 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0 000212517 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de 000212517 650_7 $$2Other$$abayesian calibration 000212517 650_7 $$2Other$$acolorectal cancer 000212517 650_7 $$2Other$$adiscrete event simulation 000212517 650_7 $$2Other$$ascreening 000212517 650_7 $$2Other$$aserrated polyps 000212517 7001_ $$0P:(DE-He78)b5d9469407737829d5348adb615655c6$$aCalderazzo, Silvia$$b1$$udkfz 000212517 7001_ $$aSchramm, Christoph$$b2 000212517 7001_ $$0P:(DE-He78)1f315d09721b91091df1ba78eb65cbaf$$aSchlander, Michael$$b3$$eLast author$$udkfz 000212517 773__ $$0PERI:(DE-600)2861432-X$$a10.1177/23814683221145701$$gVol. 8, no. 1, p. 238146832211457 -$$n1$$p238146832211457 -$$tMedical decision making policy & practice$$v8$$x2381-4683$$y2023 000212517 909CO $$ooai:inrepo02.dkfz.de:212517$$pVDB 000212517 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)d2944f54ead34dbf6fb03e359225a1b9$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ 000212517 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)b5d9469407737829d5348adb615655c6$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ 000212517 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)1f315d09721b91091df1ba78eb65cbaf$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ 000212517 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0 000212517 9141_ $$y2023 000212517 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-01-15T16:51:08Z 000212517 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-01-15T16:51:08Z 000212517 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2022-11-16 000212517 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2022-11-16 000212517 915__ $$0StatID:(DE-HGF)0430$$2StatID$$aNational-Konsortium$$d2023-10-27$$wger 000212517 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bMDM POLICY PRACT : 2022$$d2023-10-27 000212517 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-27 000212517 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-27 000212517 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-10-27 000212517 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2021-01-15T16:51:08Z 000212517 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-27 000212517 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2023-10-27 000212517 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-27 000212517 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2023-10-27 000212517 9202_ $$0I:(DE-He78)C100-20160331$$kC100$$lGesundheitsökonomie$$x0 000212517 9201_ $$0I:(DE-He78)C100-20160331$$kC100$$lGesundheitsökonomie$$x0 000212517 9201_ $$0I:(DE-He78)C060-20160331$$kC060$$lC060 Biostatistik$$x1 000212517 9200_ $$0I:(DE-He78)C100-20160331$$kC100$$lGesundheitsökonomie$$x0 000212517 980__ $$ajournal 000212517 980__ $$aVDB 000212517 980__ $$aI:(DE-He78)C100-20160331 000212517 980__ $$aI:(DE-He78)C060-20160331 000212517 980__ $$aUNRESTRICTED