Causal inference with generalized structural mean models
Causal inference with generalized structural mean models
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DOI：
10.1046/j.1369-7412.2003.00417.x
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发表时间：
2003-01-01
影响因子：
5.8
通讯作者：
Goetghebeur, E
中科院分区：
文献类型：
--
作者：
Vansteelandt, S;Goetghebeur, E
我们估计因果关系的实证研究中，暴露不完全控制，如在观察性研究或患者不遵守和自我选择的治疗开关随机临床试验。加法和乘法结构平均模型已被证明是有用的，但受到经典的限制，线性和对数线性模型时，容纳二进制数据。我们提出了广义结构均值模型来克服这些局限性。这是一个半参数两阶段模型，它扩展了结构平均模型，以处理非线性平均暴露效应。第一阶段结构模型通过对比暴露亚组人群中观察到的和潜在的无风险结果的平均值，描述了接受暴露的因果效应。为了识别的结构参数，第二阶段的“滋扰”模型的介绍。这是一个经典的关联模型的形式，在给定观察到的暴露的情况下，预期结果。在此模型下，我们推导出了结构效应的估计方程，得到了一致的、渐近正态的和有效的估计量。我们研究了它们对模型误设的鲁棒性，并在没有任何暴露效应的情况下构建了鲁棒估计。双逻辑结构均值模型更详细地开发，以估计在随机对照降压试验中观察到的暴露对治疗成功的影响，其中有自我选择的不依从性。
We estimate cause-effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to overcome these limitations. This is a semiparametric two-stage model which extends the structural mean model to handle non-linear average exposure effects. The first-stage structural model describes the causal effect of received exposure by contrasting the means of observed and potential exposure-free outcomes in exposed subsets of the population. For identification of the structural parameters, a second stage 'nuisance' model is introduced. This takes the form of a classical association model for expected outcomes given observed exposure. Under the model, we derive estimating equations which yield consistent, asymptotically normal and efficient estimators of the structural effects. We examine their robustness to model misspecification and construct robust estimators in the absence of any exposure effect. The double-logistic structural mean model is developed in more detail to estimate the effect of observed exposure on the success of treatment in a randomized controlled blood pressure reduction trial with self-selected non-compliance.