Literature DB >> 17210941

Use of modeling to evaluate the cost-effectiveness of cancer screening programs.

Amy B Knudsen1, Pamela M McMahon, G Scott Gazelle.   

Abstract

Cost-effectiveness analysis (CEA) is an analytic tool that provides a framework for comparing the health benefits and resource expenditures associated with competing medical and public health interventions, thereby allowing decision makers to identify interventions that yield the greatest amount of health, given their resource constraints. Models are important components of most, if not all, CEAs, and they play a key role in evaluating the cost-effectiveness of cancer screening programs, in particular. In this article, we describe the basic types of models used to evaluate cancer screening programs and provide examples of the use of models in CEAs and to guide cancer screening policy. Finally, we offer some suggestions for important concepts to consider when interpreting model results.

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Year:  2007        PMID: 17210941     DOI: 10.1200/JCO.2006.07.9202

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  14 in total

1.  Risk-specific optimal cancer screening schedules: an application to breast cancer early detection.

Authors:  Charlotte Hsieh Ahern; Yi Cheng; Yu Shen
Journal:  Stat Biosci       Date:  2011-12

Review 2.  Cost-effectiveness analyses of vaccination programmes : a focused review of modelling approaches.

Authors:  Sun-Young Kim; Sue J Goldie
Journal:  Pharmacoeconomics       Date:  2008       Impact factor: 4.981

3.  Comparative economic evaluation of data from the ACRIN National CT Colonography Trial with three cancer intervention and surveillance modeling network microsimulations.

Authors:  David J Vanness; Amy B Knudsen; Iris Lansdorp-Vogelaar; Carolyn M Rutter; Ilana F Gareen; Benjamin A Herman; Karen M Kuntz; Ann G Zauber; Marjolein van Ballegooijen; Eric J Feuer; Mei-Hsiu Chen; C Daniel Johnson
Journal:  Radiology       Date:  2011-08-03       Impact factor: 11.105

Review 4.  Calibration methods used in cancer simulation models and suggested reporting guidelines.

Authors:  Natasha K Stout; Amy B Knudsen; Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

5.  Estimating long-term effectiveness of lung cancer screening in the Mayo CT screening study.

Authors:  Pamela M McMahon; Chung Yin Kong; Bruce E Johnson; Milton C Weinstein; Jane C Weeks; Karen M Kuntz; Jo-Anne O Shepard; Stephen J Swensen; G Scott Gazelle
Journal:  Radiology       Date:  2008-05-05       Impact factor: 11.105

6.  Adopting helical CT screening for lung cancer: potential health consequences during a 15-year period.

Authors:  Pamela M McMahon; Chung Yin Kong; Milton C Weinstein; Angela C Tramontano; Lauren E Cipriano; Bruce E Johnson; Jane C Weeks; G Scott Gazelle
Journal:  Cancer       Date:  2008-12-15       Impact factor: 6.860

7.  Chapter 13: CISNET lung models: comparison of model assumptions and model structures.

Authors:  Pamela M McMahon; William D Hazelton; Marek Kimmel; Lauren D Clarke
Journal:  Risk Anal       Date:  2012-07       Impact factor: 4.000

8.  Comparing upper gastrointestinal X-ray and endoscopy for gastric cancer diagnosis in Korea.

Authors:  Hoo-Yeon Lee; Eun-Cheol Park; Jae-Kwan Jun; Kui-Son Choi; Myung-Il Hahm
Journal:  World J Gastroenterol       Date:  2010-01-14       Impact factor: 5.742

9.  Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity.

Authors:  Elliot Lee; Mariel S Lavieri; Michael L Volk; Yongcai Xu
Journal:  Health Care Manag Sci       Date:  2014-10-12

10.  Cost of a 5-year lung cancer survivor: symptomatic tumour identification vs proactive computed tomography screening.

Authors:  A W Castleberry; D Smith; C Anderson; A J Rotter; F W Grannis
Journal:  Br J Cancer       Date:  2009-08-18       Impact factor: 7.640

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