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Decision science and cervical cancer

  1. Scott B. Cantor Ph.D.1,
  2. Marianne C. Fahs Ph.D., M.P.H.2,
  3. Jeanne S. Mandelblatt M.D., M.P.H.3,
  4. Evan R. Myers M.D., M.P.H.4,
  5. Gillian D. Sanders Ph.D.5

Article first published online: 22 OCT 2003

DOI: 10.1002/cncr.11680

Issue

Cancer

Cancer

Special Issue: Proceedings of the Second International Conference on Cervical Cancer

Supplement: Second International Conference on Cervical Cancer

Volume 98, Issue Supplement S9, pages 2003–2008, 1 November 2003

Additional Information

How to Cite

Cantor, S. B., Fahs, M. C., Mandelblatt, J. S., Myers, E. R. and Sanders, G. D. (2003), Decision science and cervical cancer. Cancer, 98: 2003–2008. doi: 10.1002/cncr.11680

Author Information

  1. 1

    Section of Health Services Research, Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas

  2. 2

    Health Policy Research Center, Milano Graduate School, New School University, New York, New York

  3. 3

    Lombardi Cancer Center, Georgetown University Medical Center, Washington, D.C

  4. 4

    Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina

  5. 5

    Center for Primary Care and Outcomes Research, Medical Information Sciences, Stanford University, Stanford, California

Publication History

  1. Issue published online: 22 OCT 2003
  2. Article first published online: 22 OCT 2003
  3. Manuscript Accepted: 25 MAR 2003
  4. Manuscript Revised: 19 MAR 2003
  5. Manuscript Received: 31 OCT 2002

Funded by

  • National Cancer Institute. Grant Numbers: R01-CA72908-02, P01-CA82710
  • Stanford Cancer Council
  • V Foundation. Grant Number: 1JVD608
  • National Institute on Aging. Grant Number: R01-AG15340

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Keywords:

  • cervix neoplasms;
  • statistical models;
  • decision theory;
  • costs and cost analysis;
  • health care economics;
  • health policy

Abstract

Mathematical modeling is an effective tool for guiding cervical cancer screening, diagnosis, and treatment decisions for patients and policymakers. This article describes the use of mathematical modeling as outlined in five presentations from the Decision Science and Cervical Cancer session of the Second International Conference on Cervical Cancer held at The University of Texas M. D. Anderson Cancer Center, April 11–14, 2002. The authors provide an overview of mathematical modeling, especially decision analysis and cost-effectiveness analysis, and examples of how it can be used for clinical decision making regarding the prevention, diagnosis, and treatment of cervical cancer. Included are applications as well as theory regarding decision science and cervical cancer. Mathematical modeling can answer such questions as the optimal frequency for screening, the optimal age to stop screening, and the optimal way to diagnose cervical cancer. Results from one mathematical model demonstrated that a vaccine against high-risk strains of human papillomavirus was a cost-effective use of resources, and discussion of another model demonstrated the importance of collecting direct non–health care costs and time costs for cost-effectiveness analysis. Research presented indicated that care must be taken when applying the results of population-wide, cost-effectiveness analyses to reduce health disparities. Mathematical modeling can encompass a variety of theoretical and applied issues regarding decision science and cervical cancer. The ultimate objective of using decision-analytic and cost-effectiveness models is to identify ways to improve women's health at an economically reasonable cost. Cancer 2003;98(9 Suppl):2003–2008. © 2003 American Cancer Society.

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