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Clarifying Differences in Natural History between Models of Screening

Author

Listed:
  • Marjolein van Ballegooijen
  • Carolyn M. Rutter
  • Amy B. Knudsen
  • Ann G. Zauber
  • James E. Savarino
  • Iris Lansdorp-Vogelaar
  • Rob Boer
  • Eric J. Feuer
  • J. Dik F. Habbema
  • Karen M. Kuntz

Abstract

Background . Microsimulation models are important decision support tools for screening. However, their complexity makes them difficult to understand and limits realization of their full potential. Therefore, it is important to develop documentation that clarifies their structure and assumptions. The authors demonstrate this problem and explore a solution for natural history using 3 independently developed colorectal cancer screening models. Methods . The authors first project effectiveness and cost-effectiveness of colonoscopy screening for the 3 models (CRC-SPIN, SimCRC, and MISCAN). Next, they provide a conventional presentation of each model, including information on structure and parameter values. Finally, they report the simulated reduction in clinical cancer incidence following a one-time complete removal of adenomas and preclinical cancers for each model. They call this new measure the maximum clinical incidence reduction (MCLIR). Results . Projected effectiveness varies widely across models. For example, estimated mortality reduction for colonoscopy screening every 10 years from age 50 to 80 years, with surveillance in adenoma patients, ranges from 65% to 92%. Given only conventional information, it is difficult to explain these differences, largely because differences in structure make parameter values incomparable. In contrast, the MCLIR clearly shows the impact of model differences on the key feature of natural history, the dwell time of preclinical disease. Dwell times vary from 8 to 25 years across models and help explain differences in projected screening effectiveness. Conclusions . The authors propose a new measure, the MCLIR, which summarizes the implications for predicted screening effectiveness of differences in natural history assumptions. Including the MCLIR in the standard description of a screening model would improve the transparency of these models.

Suggested Citation

  • Marjolein van Ballegooijen & Carolyn M. Rutter & Amy B. Knudsen & Ann G. Zauber & James E. Savarino & Iris Lansdorp-Vogelaar & Rob Boer & Eric J. Feuer & J. Dik F. Habbema & Karen M. Kuntz, 2011. "Clarifying Differences in Natural History between Models of Screening," Medical Decision Making, , vol. 31(4), pages 540-549, July.
  • Handle: RePEc:sae:medema:v:31:y:2011:i:4:p:540-549
    DOI: 10.1177/0272989X11408915
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    1. Karen M. Kuntz & Iris Lansdorp-Vogelaar & Carolyn M. Rutter & Amy B. Knudsen & Marjolein van Ballegooijen & James E. Savarino & Eric J. Feuer & Ann G. Zauber, 2011. "A Systematic Comparison of Microsimulation Models of Colorectal Cancer," Medical Decision Making, , vol. 31(4), pages 530-539, July.
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    Cited by:

    1. Jing Voon Chen & Julia L. Higle & Michael Hintlian, 2018. "A systematic approach for examining the impact of calibration uncertainty in disease modeling," Computational Management Science, Springer, vol. 15(3), pages 541-561, October.

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