Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks Article Swipe
Vahab Vahdat
,
Oğuzhan Alagöz
,
Jing Voon Chen
,
Leila Saoud
,
Bijan J. Borah
,
Paul J. Limburg
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1177/0272989x231184175
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1177/0272989x231184175
Objectives Machine learning (ML)–based emulators improve the calibration of decision-analytical models, but their performance in complex microsimulation models is yet to be determined. Methods We demonstrated the use of an ML-based emulator with the Colorectal Cancer (CRC)-Adenoma Incidence and Mortality (CRC-AIM) model, which includes 23 unknown natural history input parameters to replicate the CRC epidemiology in the United States. We first generated 15,000 input combinations and ran the CRC-AIM model to evaluate CRC incidence, adenoma size distribution, and the percentage of small adenoma detected by colonoscopy. We then used this data set to train several ML algorithms, including deep neural network (DNN), random forest, and several gradient boosting variants (i.e., XGBoost, LightGBM, CatBoost) and compared their performance. We evaluated 10 million potential input combinations using the selected emulator and examined input combinations that best estimated observed calibration targets. Furthermore, we cross-validated outcomes generated by the CRC-AIM model with those made by CISNET models. The calibrated CRC-AIM model was externally validated using the United Kingdom Flexible Sigmoidoscopy Screening Trial (UKFSST). Results The DNN with proper preprocessing outperformed other tested ML algorithms and successfully predicted all 8 outcomes for different input combinations. It took 473 s for the trained DNN to predict outcomes for 10 million inputs, which would have required 190 CPU-years without our DNN. The overall calibration process took 104 CPU-days, which included building the data set, training, selecting, and hyperparameter tuning of the ML algorithms. While 7 input combinations had acceptable fit to the targets, a combination that best fits all outcomes was selected as the best vector. Almost all of the predictions made by the best vector laid within those from the CISNET models, demonstrating CRC-AIM’s cross-model validity. Similarly, CRC-AIM accurately predicted the hazard ratios of CRC incidence and mortality as reported by UKFSST, demonstrating its external validity. Examination of the impact of calibration targets suggested that the selection of the calibration target had a substantial impact on model outcomes in terms of life-year gains with screening. Conclusions Emulators such as a DNN that is meticulously selected and trained can substantially reduce the computational burden of calibrating complex microsimulation models. Highlights Calibrating a microsimulation model, a process to find unobservable parameters so that the model fits observed data, is computationally complex. We used a deep neural network model, a popular machine learning algorithm, to calibrate the Colorectal Cancer Adenoma Incidence and Mortality (CRC-AIM) model. We demonstrated that our approach provides an efficient and accurate method to significantly speed up calibration in microsimulation models. The calibration process successfully provided cross-model validation of CRC-AIM against 3 established CISNET models and also externally validated against a randomized controlled trial.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/0272989x231184175
- https://journals.sagepub.com/doi/pdf/10.1177/0272989X231184175
- OA Status
- hybrid
- Cited By
- 21
- References
- 92
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383998850
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- OpenAlex ID
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https://openalex.org/W4383998850Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/0272989x231184175Digital Object Identifier
- Title
-
Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-11Full publication date if available
- Authors
-
Vahab Vahdat, Oğuzhan Alagöz, Jing Voon Chen, Leila Saoud, Bijan J. Borah, Paul J. LimburgList of authors in order
- Landing page
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https://doi.org/10.1177/0272989x231184175Publisher landing page
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https://journals.sagepub.com/doi/pdf/10.1177/0272989X231184175Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://journals.sagepub.com/doi/pdf/10.1177/0272989X231184175Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Machine learning, Hyperparameter, Artificial neural network, Calibration, Microsimulation, Gradient boosting, Random forest, Data mining, Statistics, Mathematics, Engineering, Transport engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
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2025: 9, 2024: 7, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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