Generalized propensity score for estimating the average treatment effect of multiple treatments
Generalized propensity score for estimating the average treatment effect of multiple treatments
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DOI：
10.1002/sim.4168
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发表时间：
2012-03-30
影响因子：
2
通讯作者：
Li, Xiao-Song
中科院分区：
文献类型：
--
作者：
Feng, Ping;Zhou, Xiao-Hua;Li, Xiao-Song
倾向评分法广泛用于临床研究，以估计两个水平的治疗对患者结局的影响。然而，由于许多疾病的复杂性，有效的治疗往往涉及多种成分。例如，在传统中医（TCM）的实践中，有效的治疗可以包括多种成分，例如中草药、针灸和按摩疗法。在涉及中医的临床试验中，患者可以被随机分配到治疗组或对照组，但他们或他们的医生可能会对使用哪种治疗成分做出不同的选择。因此，治疗组分不是随机分配的。Rosenbaum和Rubin提出了二元治疗的倾向评分法，Imbens将他们的工作扩展到多种治疗。这些作者将广义倾向评分定义为在给定治疗前变量的情况下接受特定水平治疗的条件概率。在目前的工作中，我们采用了这种方法，并开发了一种基于广义倾向评分的统计方法，以估计在多种治疗的情况下的治疗效果。讨论并比较了两种方法：倾向评分回归调整和倾向评分加权。我们使用这些方法来评估多种治疗IMPACT临床试验中个体治疗的相对有效性。结果表明，这两种方法都表现良好，当样本量是中等或大。版权所有（c）2011约翰威利父子有限公司
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. Copyright (c) 2011 John Wiley & Sons, Ltd.