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Health Care Costs for State Transition Models in Prostate Cancer

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Listed:
  • Murray D. Krahn
  • Karen E. Bremner
  • Brandon Zagorski
  • Shabbir M. H. Alibhai
  • Wendong Chen
  • George Tomlinson
  • Nicholas Mitsakakis
  • Gary Naglie

Abstract

Objective . To obtain estimates of direct health care costs for prostate cancer (PC) from diagnosis to death to inform state transition models. Methods . A stratified random sample of PC patients residing in 3 geographically diverse regions of Ontario, Canada, and diagnosed in 1993–1994, 1997–1998, and 2001–2002, was selected from the Ontario Cancer Registry. We retrieved patients’ pathology reports to identify referring physicians and contacted surviving patients and next of kin of deceased patients for informed consent. We reviewed clinic charts to obtain data required to allocate each patient’s observation time to 11 PC-specific health states. We linked these data to health care administrative databases to calculate resource use and costs (Canadian dollars, 2008) per health state. A multivariable mixed-effects model determined predictors of costs. Results . The final sample numbered 829 patients. In the regression model, total direct costs increased with age, comorbidity, and Gleason score (all P

Suggested Citation

  • Murray D. Krahn & Karen E. Bremner & Brandon Zagorski & Shabbir M. H. Alibhai & Wendong Chen & George Tomlinson & Nicholas Mitsakakis & Gary Naglie, 2014. "Health Care Costs for State Transition Models in Prostate Cancer," Medical Decision Making, , vol. 34(3), pages 366-378, April.
    • Handle: RePEc:sae:medema:v:34:y:2014:i:3:p:366-378
    DOI: 10.1177/0272989X13493970
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    References listed on IDEAS

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    1. Laurent Molinier & Christel Castelli & Eric Bauvin & Xavier Rebillard & Michel Soulié & Jean-Pierre Daurès & Pascale Grosclaude, 2011. "Cost study of the clinical management of prostate cancer in France: results on the basis of population-based data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(4), pages 363-371, August.
    2. Nicola J. Cooper & Paul C. Lambert & Keith R. Abrams & Alexander J. Sutton, 2007. "Predicting costs over time using Bayesian Markov chain Monte Carlo methods: an application to early inflammatory polyarthritis," Health Economics, John Wiley & Sons, Ltd., vol. 16(1), pages 37-56, January.
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    1. Thomas Grochtdreis & Hans-Helmut König & Alexander Dobruschkin & Gunhild von Amsberg & Judith Dams, 2018. "Cost-effectiveness analyses and cost analyses in castration-resistant prostate cancer: A systematic review," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-25, December.
    2. Xuanqian Xie & Alexis K. Schaink & Sichen Liu & Myra Wang & Andrei Volodin, 2023. "Understanding bias in probabilistic analysis in model-based health economic evaluation," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(2), pages 307-319, March.

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