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

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WOS被引频次:41
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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, XS
作者机构:
[Fan, Ming-Yu] Univ Washington, Dept Psychiat & Behav Sci, Seattle, WA 98195 USA.
[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.
[Zou, Qing-Ming] Nanhua Univ, Sch Econ & Management, Hengyang, Hunan, Peoples R China.
通讯机构:
[Li, Xiao-Song] 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
文献类别:
WOS:Article
所属学科:
ESI学科类别:社会科学,概论;WOS学科类别:Mathematical & Computational Biology;Medical Informatics;Medicine, Research & Experimental;Public, Environmental & Occupational Health;Statistics & Probability
入藏号:
PMID:21351291
基金类别:
National 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:
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 they or their doctors may make different choices about which treatment component to use. As a result, treatment components are not randomly assigned. Rosenbaum and Rubin proposed the propensity score method for binary treatments, and Imbens extended their work to multiple treatments. These authors defined the generalized propensity score as the conditional probability of receiving a particular level of the treatment given the pre-treatment variables. In the present work, we adopted this approach and developed a statistical methodology based on the generalized propensity score in order to estimate treatment effects in the case of multiple treatments. Two methods were discussed and compared: propensity score regression adjustment and propensity score weighting. We used these methods to assess the relative effectiveness of individual treatments in the multiple-treatment IMPACT clinical trial. The results reveal that both methods perform well when the sample size is moderate or large.
参考文献:
Angrist JD, 1996, J AM STAT ASSOC, V91, P444, DOI 10.2307/2291629
Anstrom KJ, 2001, BIOMETRICS, V57, P1207, DOI 10.1111/j.0006-341X.2001.01207.x
Austin PC, 2006, STAT MED, V25, P2084, DOI 10.1002/sim.2328
Austin PC, 2005, STAT MED, V24, P1563, DOI 10.1002/sim.2053
Austin PC, 2008, PHARMACOEPIDEM DR S, V17, P1202, DOI 10.1002/pds.1673

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