乳腺癌辅助化学治疗方案TAC与FAC的成本效果分析  被引量:10

Cost-effectiveness analysis of adjuvant therapy for breast cancer:TAC vs. FAC

在线阅读下载全文

作  者:罗霞[1,2] 彭六保[1] 万小敏[1] 谭重庆[1] 崔巍[1] 曹俊华[1,2] 

机构地区:[1]中南大学湘雅二医院药学部,湖南长沙410011 [2]中南大学药学院,湖南长沙410011

出  处:《中国新药与临床杂志》2010年第3期232-236,共5页Chinese Journal of New Drugs and Clinical Remedies

基  金:湖南省软科学项目(2008ZK3039)

摘  要:目的对乳腺癌辅助化学治疗(化疗)方案多西他赛联合多柔比星及环磷酰胺(TAC方案)与氟尿嘧啶联合多柔比星及环磷酰胺(FAC方案)进行药物经济学分析。方法建立Markov模型,对接受TAC与FAC辅助化疗方案的患者进行模拟,综合应用临床试验BCIRG 001研究结果、其他公开发表的文献和某大型综合性医院的病历资料,评价2种方案的成本效果比,并进行敏感度分析。结果TAC组的5年健康结果值为4.043 QALYs,比FAC组多0.078 QALYs;TAC组的成本为104597.23元,比FAC组多4305.50元,增量成本-效果比为55016元/QALY。一元敏感度及概率敏感度分析显示Markov模型对重要参数都较稳定。结论从中国医疗卫生体系角度出发,5年内辅助化疗方案TAC比FAC对早期淋巴结阳性乳腺癌患者更具成本效果比。AIM To analyze the cost-effectiveness of docetaxel / doxorubicin / cyclophosphamide (TAC) and fluorouracil / doxorubicin / cyclophosphamide (FAC). METHODS A Markov model was built to simulate the process of breast cancer events and death occurred in both TAC and FAC armed patients. Data were collected from the clinical trial BCIRG 001, some publicized literatures and patients' diaries of a large Chinese comprehensive hospital. The cost-effectiveness analysis of two regimens and sensitvity analysis were performed using a Markov model. RESULTS The 5 years'results showed that patients receiving TAC gained 4.043 QALYs which were 0.078 QALYs more than patients receiving FAC. The ICER value was 55 016 Yuan / QALY. The costs of patients receiving TAC were 104 597.23 Yuan which were 4 305.50 Yuan more than that of FAC. Theresults were robust to key parameters in both one-way sensitivity analysis and probabilistic sensitivity analysis. CONCLUSION Compared with FAC regimen, TAC can be viewed as cost-effective for node-positive early breast cancer patients in 5 years horizon from a Chinese health system perspective.

关 键 词:乳腺肿瘤 化学疗法 辅助 经济学 药学 MARKOV模型 成本效果分析 

分 类 号:R956[医药卫生—药学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象