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减少医疗保健资源分配中的不确定性。

Reducing uncertainty in health-care resource allocation.

作者信息

Simonsson T, Sjölund K, Bümming P, Ahlman H, Nilsson B, Odén A

机构信息

Department of Biomedicine, Sahlgrenska Academy at Göteborg University, Göteborg SE-405 30, Sweden.

出版信息

Br J Cancer. 2007 Jun 18;96(12):1834-8. doi: 10.1038/sj.bjc.6603795. Epub 2007 May 22.

DOI:10.1038/sj.bjc.6603795
PMID:17519908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2359979/
Abstract

A key task for health policymakers is to optimise the outcome of health care interventions. The pricing of a new generation of cancer drugs, in combination with limited health care resources, has highlighted the need for improved methodology to estimate outcomes of different treatment options. Here we introduce new general methodology, which for the first time employs continuous hazard functions for analysis of survival data. Access to continuous hazard functions allows more precise estimations of survival outcomes for different treatment options. We illustrate the methodology by calculating outcomes for adjuvant treatment of gastrointestinal stromal tumours with imatinib mesylate, which selectively inhibits the activity of a cancer-causing enzyme and is a hallmark representative for the new generation of cancer drugs. The calculations reveal that optimal drug pricing can generate all win situations that improve drug availability to patients, make the most of public expenditure on drugs and increase pharmaceutical company gross profits. The use of continuous hazard functions for analysis of survival data may reduce uncertainty in health care resource allocation, and the methodology can be used for drug price negotiations and to investigate health care intervention thresholds. Health policy makers, pharmaceutical industry, reimbursement authorities and insurance companies, as well as clinicians and patient organisations, should find the methodology useful.

摘要

卫生政策制定者的一项关键任务是优化医疗保健干预措施的效果。新一代癌症药物的定价,再加上有限的医疗保健资源,凸显了改进方法以评估不同治疗方案效果的必要性。在此,我们介绍一种新的通用方法,该方法首次采用连续风险函数来分析生存数据。使用连续风险函数能够更精确地估计不同治疗方案的生存结果。我们通过计算甲磺酸伊马替尼辅助治疗胃肠道间质瘤的结果来说明该方法,甲磺酸伊马替尼可选择性抑制一种致癌酶的活性,是新一代癌症药物的典型代表。计算结果表明,最优的药物定价能够产生多方共赢的局面,即提高患者获得药物的可及性,充分利用药物方面的公共支出,并增加制药公司的毛利润。使用连续风险函数分析生存数据可能会减少医疗保健资源分配中的不确定性,并且该方法可用于药品价格谈判以及研究医疗保健干预阈值。卫生政策制定者、制药行业、报销机构和保险公司,以及临床医生和患者组织,应该会发现该方法很有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b0/2359979/07f4cdfaa952/6603795f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b0/2359979/07f4cdfaa952/6603795f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b0/2359979/07f4cdfaa952/6603795f1.jpg

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Causes of cancer in the world: comparative risk assessment of nine behavioural and environmental risk factors.全球癌症病因:九种行为和环境风险因素的比较风险评估
Lancet. 2005 Nov 19;366(9499):1784-93. doi: 10.1016/S0140-6736(05)67725-2.
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Effect of the tyrosine kinase inhibitor STI571 in a patient with a metastatic gastrointestinal stromal tumor.酪氨酸激酶抑制剂STI571对一名转移性胃肠道间质瘤患者的疗效。
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