IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v38y2018i8p917-929.html
   My bibliography  Save this article

Modeling in Colorectal Cancer Screening: Assessing External and Predictive Validity of MISCAN-Colon Microsimulation Model Using NORCCAP Trial Results

Author

Listed:
  • Maaike Buskermolen
  • Andrea Gini
  • Steffie K. Naber
  • Esther Toes-Zoutendijk
  • Harry J. de Koning
  • Iris Lansdorp-Vogelaar

Abstract

Background. Microsimulation models are increasingly being used to inform colorectal cancer (CRC) screening recommendations. MISCAN-Colon is an example of such a model, used to inform the Dutch CRC screening program and US Preventive Services Task Force guidelines. Assessing the validity of these models is essential to provide transparency regarding their performance. In this study, we tested the external and predictive validity of MISCAN-Colon. Methods. We validated MISCAN-Colon using the Norwegian Colorectal Cancer Prevention (NORCCAP) trial, a randomized controlled trial that examined the effectiveness of once-only flexible sigmoidoscopy (FS) screening. We simulated the study population and design of the NORCCAP trial in MISCAN-Colon and compared 10- to 12-year model-predicted hazard ratios (HRs) for overall and distal CRC incidence and mortality to those observed. In addition, we compared the numbers of screen-detected neoplasia. Finally, we predicted the trial’s future results to allow for the assessment of predictive validity. Results. MISCAN-Colon predicted an HR for overall CRC incidence (0.85), distal CRC incidence (0.82), overall CRC mortality (0.68), and distal CRC mortality (0.62). These were within the limits of the 95% confidence intervals of the NORCCAP trial results. Similar results were observed for the number of screen-detected cancers. The model significantly underestimated the number of screen-detected adenomas. Model-predicted HRs for CRC incidence and mortality up to 15- to 17-year follow-up were 0.84 and 0.72, respectively. Conclusion. Although the underestimation of screen-detected adenomas requires further investigation, MISCAN-Colon is able to make a valid replication of the CRC incidence and mortality reduction of an FS screening trial, which suggests that it can be considered a useful tool to support decision making on CRC screening.

Suggested Citation

  • Maaike Buskermolen & Andrea Gini & Steffie K. Naber & Esther Toes-Zoutendijk & Harry J. de Koning & Iris Lansdorp-Vogelaar, 2018. "Modeling in Colorectal Cancer Screening: Assessing External and Predictive Validity of MISCAN-Colon Microsimulation Model Using NORCCAP Trial Results," Medical Decision Making, , vol. 38(8), pages 917-929, November.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:8:p:917-929
    DOI: 10.1177/0272989X18806497
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X18806497
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X18806497?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kevin ten Haaf & Jihyoun Jeon & Martin C Tammemägi & Summer S Han & Chung Yin Kong & Sylvia K Plevritis & Eric J Feuer & Harry J de Koning & Ewout W Steyerberg & Rafael Meza, 2017. "Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study," PLOS Medicine, Public Library of Science, vol. 14(4), pages 1-24, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fuxiao Li & Xiang Li & Chuanhai Guo & Ruiping Xu & Fenglei Li & Yaqi Pan & Mengfei Liu & Zhen Liu & Chao Shi & Hui Wang & Minmin Wang & Hongrui Tian & Fangfang Liu & Ying Liu & Jingjing Li & Hong Cai , 2019. "Estimation of Cost for Endoscopic Screening for Esophageal Cancer in a High-Risk Population in Rural China: Results from a Population-Level Randomized Controlled Trial," PharmacoEconomics, Springer, vol. 37(6), pages 819-827, June.
    2. Marcela Fu & Noémie Travier & Juan Carlos Martín-Sánchez & Jose M Martínez-Sánchez & Carmen Vidal & Montse Garcia & on behalf of the LUCAPREV research group, 2018. "Identifying high-risk individuals for lung cancer screening: Going beyond NLST criteria," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-11, April.
    3. Steven B. Markowitz, 2022. "Lung Cancer Screening in Asbestos-Exposed Populations," IJERPH, MDPI, vol. 19(5), pages 1-15, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:38:y:2018:i:8:p:917-929. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.