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Generalized propensity score for estimating the average treatment effect of multiple treatments

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成果类型:
期刊论文
作者:
Feng, Ping;Zhou, Xiao-Hua;Zou, Qing-Ming;Fan, Ming-Yu;Li, Xiao-Song*
通讯作者:
Li, Xiao-Song
作者机构:
[Feng, Ping] Sichuan Univ, W China Hosp, Inst Clin Trials, Chengdu, Sichuan, Peoples R China.
[Li, Xiao-Song] Sichuan Univ, W China Sch Publ Hlth, Chengdu, Sichuan, Peoples R China.
[Zhou, Xiao-Hua] Harbin Med Coll, Harbin, Peoples R China.
[Zhou, Xiao-Hua] Univ Washington, Sch Publ Hlth, Dept Biostat, Seattle, WA 98195 USA.
[Zhou, Xiao-Hua] Peking Univ, Beijing Int Ctr Math Res, Beijing 100871, Peoples R China.
通讯机构:
[Li, Xiao-Song] S
Sichuan Univ, W China Sch Publ Hlth, Chengdu, Sichuan, Peoples R China.
语种:
英文
关键词:
causal inference;generalized propensity score;treatment effect;Traditional Chinese Medicine (TCM);multiple treatment components
期刊:
Statistics in Medicine
ISSN:
0277-6715
年:
2012
卷:
31
期:
7
页码:
681-697
基金类别:
National Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [30728019]; Department of Science and Technology of Sichuan Province, People's Republic of China [2009JY0020]
机构署名:
本校为其他机构
院系归属:
管理学院
摘要:
Abstract The propensity score method is widely used in clinical studies to estimate the effect of a treatment with two levels on patient's outcomes. However, due to the complexity of many diseases, an effective treatment often involves multiple components. For example, in the practice of Traditional Chinese Medicine (TCM), an effective treatment may include multiple components, e.g. Chinese herbs, acupuncture, and massage therapy. In clinical trials involving TCM, patients could be randomly assigned to either the treatment or control group, but...

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