Assessing the cost effectiveness of adjuvant therapies in early breast cancer using a decision analysis model
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Background: We have developed a decision analysis model that uses the results of available randomized controlled trials to model the natural history of early breast cancer and assess the potential clinical and financial effects of using adjuvant therapies.
Patients and Methods: The original model was used to assess the impact of chemotherapy in hypothetical groups of 45-year-old and 60-year-old node-negative, estrogen receptor-negative women. Using the 1992 Early Breast Cancer Trialists' Collaborative Group report, we have expanded and revised the model to assess: 1) the role of tamoxifen alone, chemotherapy alone, or combined therapy in pre-menopausal women, and 2) chemotherapy in elderly women with node-negative, estrogen receptor-negative cancer.
Results: For pre-menopausal women, we found that chemotherapy increases quality adjusted life expectancy and survival by a substantial amount at a cost less than most accepted medical interventions. Combined therapy is beneficial and cost-effective in estrogen receptor-positive cancer. For the elderly, chemotherapy prolongs survival but to a lesser extent compared to younger women. The cost of this benefit is high but within the range of commonly reimbursed procedures for women under age 75 without other co-existing conditions.
Conclusions: For most patients some form of adjuvant therapy is beneficial and cost-effective. The model builds upon the data derived from collaborative efforts assessing the effectiveness of adjuvant therapies. The model highlights the need for an equal commitment to assessing the economic and quality of life impacts of breast cancer treatments.
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- Assessing the cost effectiveness of adjuvant therapies in early breast cancer using a decision analysis model
Breast Cancer Research and Treatment
Volume 25, Issue 2 , pp 97-105
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- breast neoplasm
- analysis decision trees
- medical effectiveness
- patient preferences
- Industry Sectors
- Author Affiliations
- 1. Division of General Medicine, Department of Internal Medicine, Medical College of Virginia, Virginia Commonwealth University, Box 170, 23298-0170, Richmond, VA, USA
- 2. Division of Hematology and Oncology, Department of Internal Medicine, Medical College of Virginia, Virginia Commonwealth University, Box 170, 23298-0170, Richmond, VA, USA