The Annals of Applied Statistics

Microsimulation model calibration using incremental mixture approximate Bayesian computation

Carolyn M. Rutter, Jonathan Ozik, Maria DeYoreo, and Nicholson Collier

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text


Microsimulation models (MSMs) are used to inform policy by predicting population-level outcomes under different scenarios. MSMs simulate individual-level event histories that mark the disease process (such as the development of cancer) and the effect of policy actions (such as screening) on these events. MSMs often have many unknown parameters; calibration is the process of searching the parameter space to select parameters that result in accurate MSM prediction of a wide range of targets. We develop Incremental Mixture Approximate Bayesian Computation (IMABC) for MSM calibration which results in a simulated sample from the posterior distribution of model parameters given calibration targets. IMABC begins with a rejection-based ABC step, drawing a sample of points from the prior distribution of model parameters and accepting points that result in simulated targets that are near observed targets. Next, the sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions and accepting points that result in accurate predictions. Posterior estimates are obtained by weighting the final set of accepted points to account for the adaptive sampling scheme. We demonstrate IMABC by calibrating CRC-SPIN 2.0, an updated version of a MSM for colorectal cancer (CRC) that has been used to inform national CRC screening guidelines.

Article information

Ann. Appl. Stat., Volume 13, Number 4 (2019), 2189-2212.

Received: August 2018
Revised: June 2019
First available in Project Euclid: 28 November 2019

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Adaptive ABC agent-based models colorectal cancer


Rutter, Carolyn M.; Ozik, Jonathan; DeYoreo, Maria; Collier, Nicholson. Microsimulation model calibration using incremental mixture approximate Bayesian computation. Ann. Appl. Stat. 13 (2019), no. 4, 2189--2212. doi:10.1214/19-AOAS1279.

Export citation


  • Beaumont, M. A., Cornuet, J.-M., Marin, J.-M. and Robert, C. P. (2009). Adaptive approximate Bayesian computation. Biometrika 96 983–990.
  • Blatt, L. J. (1961). Polyps of the colon and rectum: Incidence and distribution. Dis. Colon Rectum 4 277–282.
  • Blum, M. G. B. and François, O. (2010). Non-linear regression models for approximate Bayesian computation. Stat. Comput. 20 63–73.
  • Bombi, J. A. (1988). Polyps of the colon in Barcelona, Spain. Cancer 61 1472–1476.
  • Chapman, I. (1963). Adenomatous polypi of large intestine: Incidence and distribution. Ann. Surg. 157 223–226.
  • Chen, T. H. H., Yen, M. F., Lai, M. S., Koong, S. L., Wang, C. Y., Wong, J. M., Prevost, T. C. and Duffy, S. W. (1999). Evaluation of a selective screening for colorectal carcinoma: The Taiwan multicenter cancer screening (TAMACS) project. Cancer 86 1116–1128.
  • Church, J. M. (2004). Clinical significance of small colorectal polyps. Dis. Colon Rectum 47 481–485.
  • Conlan, A. J. K., McKinley, T. J., Karolemeas, K., Pollock, E. B., Goodchild, A. V., Mitchell, A. P., Birch, C. P. D., Clifton-Hadley, R. S. and Wood, J. L. N. (2012). Estimating the hidden burden of bovine tuberculosis in Great Britain. PLoS Comput. Biol. 8 e1002730.
  • Corley, D. A., Jensen, C. D., Marks, A. R., Zhao, W. K., de Boer, J., Levin, T. R., Doubeni, C., Fireman, B. H. and Quesenberry, C. P. (2013). Variation of adenoma prevalence by age, sex, race, and colon location in a large population: Implications for screening and quality programs. Clin. Gastroenterol. Hepatol. 11 172–180.
  • de Koning, H. J., Meza, R., Plevritis, S. K., Ten Haaf, K., Munshi, V. N., Jeon, J., Erdogan, S. A., Kong, C. Y., Han, S. S. et al. (2014). Benefits and harms of computed tomography lung cancer screening strategies: A comparative modeling study for the US Preventive Services Task Force. Ann. Intern. Med. 160 311–320.
  • Eide, T. J. and Stalsberg, H. (1978). Polyps of the large intestine in Northern Norway. Cancer 42 2839–2848.
  • Centers for Disease Control (2011). Vital signs: Colorectal cancer screening, incidence, and mortality—United States, 2002–2010. Morb. Mortal. Wkly. Rep. 60 884–889.
  • Frazier, D. T., Martin, G. M., Robert, C. P. and Rousseau, J. (2018). Asymptotic properties of approximate Bayesian computation. Biometrika 105 593–607.
  • Hakulinen, T. (1977). On long-term relative survival rates. J. Clin. Epidemiol. 30 431–443.
  • Hixson, L., Fennerty, M., Sampliner, R., McGee, D. and Garewal, H. (1990). Prospective study of the frequency and size distribution of polyps missed by colonoscopy. J. Natl. Cancer Inst. 82 1769–1772.
  • Imperiale, T. F., Wagner, D. R., Lin, C. Y., Larkin, G. N., Rogge, J. D. and Ransohoff, D. F. (2000). Risk of advanced proximal neoplasms in asymptomatic adults according to the distal colorectal findings. N. Engl. J. Med. 343 169–174.
  • Johannsen, L. G. K., Momsen, O. and Jacobsen, N. O. (1989). Polyps of the large intestine in Aarhus, Demark. An autopsy study. Cancer 24 799–806.
  • Kim, J. J., Burger, E. A., Regan, C. and Sy, S. (2017). Screening for cervical cancer in primary care: a decision analysis for the U.S. Preventive Services Task Force. Technical Report. Agency for Healthcare Research and Quality. Available at, Contract No. HHSA-290-2012-00015-I. Last accessed on February 1, 2019.
  • Kish, L. (1965). Survey Sampling. Wiley, New York, USA.
  • Knudsen, A. B., Zauber, A. G., Rutter, C. M., Naber, S. K., Doria-Rose, V. P., Pabiniak, C., Johanson, C., Fischer, S. E., Lansdorp-Vogelaar, I. et al. (2016). Estimation of benefits, burden, and harms of colorectal cancer screening strategies: Modeling study for the US Preventive Services Task Force. J. Am. Med. Dir. Assoc. 315 2595–2609.
  • Koh, K.-J., Lin, L.-H., Huang, S.-H. and Wong, J.-U. (2015). CARE—pediatric colon adenocarcinoma: A case report and literature review comparing differences in clinical features between children and adult patients. Medicine (Baltim. Md.) 94 e503.
  • Kong, C. Y., McMahon, P. M. and Gazelle, G. S. (2009). Calibration of disease simulation model using an engineering approach. Value Health 12 521–529.
  • Leslie, A., Carey, F. A., Pratt, N. R. and Steele, R. J. C. (2002). The colorectal adenoma–carcinoma sequence. Br. J. Surg. 89 845–860.
  • Li, W. and Fearnhead, P. (2018). On the asymptotic efficiency of approximate Bayesian computation estimators. Biometrika 105 285–299.
  • Lieberman, D., Moravec, M., Holub, J., Michaels, L. and Eisen, G. (2008). Polyp size and advanced histology in patients undergoing colonoscopy screening: Implications for CT colonography. Gastroenterology 135 1100–1105.
  • Liu, J. S. (2001). Monte Carlo Strategies in Scientific Computing. Springer Series in Statistics. Springer, New York.
  • Mandelblatt, J. S., Stout, N. K., Schechter, C. B., Van Den Broek, J. J., Miglioretti, D. L., Krapcho, M., Trentham-Dietz, A., Munoz, D., Lee, S. J. et al. (2016). Collaborative modeling of the benefits and harms associated with different US breast cancer screening strategies. Ann. Intern. Med. 164 215–225.
  • Marin, J.-M., Pudlo, P., Robert, C. P. and Ryder, R. J. (2012). Approximate Bayesian computational methods. Stat. Comput. 22 1167–1180.
  • Marjoram, P., Molitor, J., Plagnol, V. and Simon, T. (2003). Markov chain Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. USA 100 15324–15328.
  • McKinley, T. J., Vernon, I., Andrianakis, I., McCreesh, N., Oakley, J. E., Nsubuga, R. N., Goldstein, M. and White, R. G. (2018). Approximate Bayesian computation and simulation-based inference for complex stochastic epidemic models. Statist. Sci. 33 4–18.
  • Meissner, H. I., Breen, N., Klabunde, C. N. and Vernon, S. W. (2006). Patterns of colorectal cancer screening uptake among men and women in the United States. Cancer Epidemiol. Biomark. Prev. 15 389–394.
  • Muto, T., Bussey, H. J. R. and Morson, B. C. (1975). The evolution of cancer in the colon and rectum. Cancer 36 2251–2270.
  • National Cancer Institute (2004). Surveillance, epidemiology, and end results (SEER) program. Available at, SEER Stat Database: Incidence—SEER 9 Regs Public-Use, Nov 2003 Sub (1973-2001), released April 2004, based on the November 2003 submission.
  • National Cancer Institute (2018). Cancer INtervention and Surveillance Modeling Network (CISNET). Available at Last accessed on February 1, 2019.
  • National Center for Health Statistics (2000). US Life Tables.
  • Nelder, J. A. and Mead, R. (1965). A simplex method for function minimization. Comput. J. 7 308–313.
  • Ozik, J., Collier, N. T., Wozniak, J. M. and Spagnuolo, C. (2016). From desktop to large-scale model exploration with Swift/T. In Proceedings of the 2016 Winter Simulation Conference (WSC) 206–220. IEEE Press.
  • Pickhardt, P. J., Choi, R., Hwang, I., Butler, J. A., Puckett, M. L., Hildebrandt, H. A., Wong, R. K., Nugent, P. A., Mysliwiec, P. A. et al. (2003). Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults. N. Engl. J. Med. 349 2191–2200.
  • Ponugoti, P. L. and Rex, D. K. (2017). Yield of a second screening colonoscopy 10 years after an initial negative examination in average-risk individuals. Gastroint. Endosc. 85 221–224.
  • Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A. and Feldman, M. W. (1999). Population growth of human Y chromosomes: A study of Y chromosome microsatellites. Mol. Biol. Evol. 16 1791–1798.
  • R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at Last accessed on February 1, 2019.
  • Raftery, A. E. and Bao, L. (2010). Estimating and projecting trends in HIV/AIDS generalized epidemics using incremental mixture importance sampling. Biometrics 66 1162–1173.
  • Ratmann, O., Camacho, A., Meijer, A. and Donker, G. (2014). Statistical modelling of summary values leads to accurate approximate Bayesian computations. Available at arXiv:1305.4283.
  • Rex, D., Cutler, C., Lemmel, G., Rahmani, E., Clark, D., Helper, D., Lehman, G. and Mark, D. (1997). Colonoscopic miss rates of adenomas determined by back-to-back colonoscopies. Gastroenterology 112 24–28.
  • Rickert, R. R., Auerbach, O., Garfinkel, L., Hammond, E. C. and Frasca, J. M. (1979). Adenomatous lesions of the large bowel. An autopsy survey. Cancer 43 1847–1857.
  • Rubin, D. (1987). The calculation of posterior distributions by data augmentation: Comment: A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: The SIR algorithm. J. Amer. Statist. Assoc. 82 543–546.
  • Rutter, C. M., Miglioretti, D. L. and Savarino, J. E. (2009). Bayesian calibration of microsimulation models. J. Amer. Statist. Assoc. 104 1338–1350.
  • Rutter, C. M. and Savarino, J. E. (2010). An evidence-based microsimulation model for colorectal cancer: Validation and application. Cancer Epidemiol. Biomark. Prev. 1055–9965.
  • Rutter, C. M., Johnson, E. A., Feuer, E. J., Knudsen, A. B., Kuntz, K. M. and Schrag, D. (2013). Secular trends in colon and rectal cancer relative survival. J. Natl. Cancer Inst. 105 1806–1813.
  • Rutter, C. M., Knudsen, A. B., Marsh, T. L., Doria-Rose, V. P., Johnson, E., Pabiniak, C., Kuntz, K. M., van Ballegooijen, M., Zauber, A. G. et al. (2016). Validation of models used to inform colorectal cancer screening guidelines: Accuracy and implications. Med. Decis. Mak. 36 604–614.
  • Sisson, S. A., Fan, Y. and Beaumont, M. A. (2019). Overview of ABC. In Handbook of Approximate Bayesian Computation. Chapman & Hall/CRC Handb. Mod. Stat. Methods 3–54. CRC Press, Boca Raton, FL.
  • Sisson, S. A., Fan, Y. and Tanaka, M. M. (2007). Sequential Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. USA 104 1760–1765.
  • Steele, R. J., Raftery, A. E. and Emond, M. J. (2006). Computing normalizing constants for finite mixture models via incremental mixture importance sampling (IMIS). J. Comput. Graph. Statist. 15 712–734.
  • Stemmermann, G. N. and Yatani, R. (1973). Diverticulosis and polyps of the large intestine. A necropsy study of Hawaii Japanese. Cancer 31 1260–1270.
  • Szczepanski, W., Urban, A. and Wierzchowski, W. (1992). Colorectal polyps in autopsy material. Part I. Adenomatous polyps. Patol. Pol. 43 79–85.
  • Tavare, S., Balding, D., Griffiths, R. and Donnelly, P. (1997). Inferring coalescence times from DNA sequence data. Genetics 145 505–518.
  • Tjørve, E. and Tjørve, K. M. (2010). A unified approach to the Richards-model family for use in growth analyses: Why we need only two model forms. J. Theoret. Biol. 267 417–425.
  • Toni, T., Welch, D., Strelkowa, N. and Stumpf, M. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J. R. Soc. Interface 6 187–202.
  • Williams, A. R., Balasooriya, B. A. W. and Day, D. W. (1982). Polyps and cancer of the large bowel: A necropsy study in Liverpool. Gut 23 835–842.
  • Winawer, S. J., Fletcher, R. H., Miller, L., Godlee, F., Stolar, M., Mulrow, C., Woolf, S., Glick, S., Ganiats, T. et al. (1997). Colorectal cancer screening: Clinical guidelines and rationale. Gastroenterology 112 594–642.
  • Wozniak, J. M., Armstrong, T. G., Wilde, M., Katz, D. S., Lusk, E. and Foster, I. T. (2013). Swift/T: Large-scale application composition via distributed-memory dataflow processing. In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing 95–102. IEEE.
  • Zauber, A. G., Knudsen, A. B., Rutter, C. M., Lansdorp-Vogelaar, I., Savarino, J. E., van Ballegooijen, M. and Kuntz, K. M. (2009). Cost-Effectiveness of CT colonography to screen for colorectal cancer: Report to the Agency for Healthcare Research and Quality from the Cancer Intervention and Surveillance Modeling Network (CISNET) for MISCAN, SimCRC, and CRC-SPIN Models. Technical Report. Available at, Project ID: CTCC0608, Last accessed on February 1, 2019.