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| 024 | 7 | _ | |a 10.1177/23814683221145701 |2 doi |
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| 037 | _ | _ | |a DKFZ-2023-00202 |
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| 100 | 1 | _ | |a Cheng, Chih-Yuan |0 P:(DE-He78)d2944f54ead34dbf6fb03e359225a1b9 |b 0 |e First author |
| 245 | _ | _ | |a Modeling 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. |
| 260 | _ | _ | |a London |c 2023 |b Sage Publishing |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1675070763_21995 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a #EA:C100#LA:C100# |
| 520 | _ | _ | |a Background. 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. |
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| 650 | _ | 7 | |a bayesian calibration |2 Other |
| 650 | _ | 7 | |a colorectal cancer |2 Other |
| 650 | _ | 7 | |a discrete event simulation |2 Other |
| 650 | _ | 7 | |a screening |2 Other |
| 650 | _ | 7 | |a serrated polyps |2 Other |
| 700 | 1 | _ | |a Calderazzo, Silvia |0 P:(DE-He78)b5d9469407737829d5348adb615655c6 |b 1 |u dkfz |
| 700 | 1 | _ | |a Schramm, Christoph |b 2 |
| 700 | 1 | _ | |a Schlander, Michael |0 P:(DE-He78)1f315d09721b91091df1ba78eb65cbaf |b 3 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1177/23814683221145701 |g Vol. 8, no. 1, p. 238146832211457 - |0 PERI:(DE-600)2861432-X |n 1 |p 238146832211457 - |t Medical decision making policy & practice |v 8 |y 2023 |x 2381-4683 |
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