A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
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DOI：
10.1177/2167696815621645
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发表时间：
2016-02-01
影响因子：
2.6
通讯作者：
Ong, Anthony D.
中科院分区：
文献类型：
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作者：
Thoemmes, Felix;Ong, Anthony D.
刚成年期的研究人员通常对发展性任务的影响感兴趣。大多数发生在成年早期/成年初期的转变不是随机的；因此，它们对发育轨迹的影响由于混淆而受到潜在偏差的影响。传统上，使用回归调整来解决混淆问题；然而，也有可行的替代方案，如倾向得分匹配和处理加权逆概率。倾向得分是在观察到的协变量上选择给定值的治疗方法的概率。治疗权重的逆概率也基于治疗选择的估计概率，并可用于创建所谓的伪种群，其中混杂因素和治疗彼此无关。在纵向模型中，这种加权可以发生在多个时间点。这篇文章提供了这些加权方法的入门，并说明了它们在成年初显期研究中的应用。我们为SPSS和R提供了注释的计算机代码，用于二进制和连续处理。
Emerging adulthood researchers are often interested in the effects of developmental tasks. The majority of transitions that occur during the period of early/emerging adulthood are not randomized; therefore, their effects on developmental trajectories are subject to potential bias due to confounding. Traditionally, confounding has been addressed using regression adjustment; however, there are viable alternatives, such as propensity score matching and inverse probability of treatment weighting. Propensity scores are probabilities of selecting treatment given values on observed covariates. Inverse probability of treatment weights are also based on estimated probabilities of treatment selection and can be used to create so-called pseudo-populations in which confounders and treatment are unrelated to each other. In longitudinal models, such weighting can occur at multiple time points. This article provides a primer on these weighting methods and illustrates their application to studies of emerging adulthood. We provide annotated computer code for both SPSS and R, for both binary and continuous treatments.