Fitting marginal structural models: estimating covariate-treatment associations in the reweighted data set can guide model fitting
Fitting marginal structural models: estimating covariate-treatment associations in the reweighted data set can guide model fitting
复制标题
DOI：
10.1016/j.jclinepi.2007.10.024
复制
发表时间：
2008-09-01
影响因子：
7.2
通讯作者：
Feldman, Brian M.
中科院分区：
文献类型：
--
作者：
Pullenayegum, Eleanor M.;Lam, Catherine;Feldman, Brian M.
目的：边际结构模型（msm）是一种日益流行的框架，其中观测数据被重新加权以得出因果推论。MSM实现这一目标的能力关键在于治疗不受治疗逆概率加权数据集中协变量的干扰。本文讨论了在处理变量和协变量都是时间相关的情况下，如何评价权重的这一重要性质。通常治疗效果的估计对权重的选择很敏感。提出了一个框架来探讨这种敏感性的原因，并选择一个好的加权方案。研究设计和背景：通过静脉注射免疫球蛋白治疗青少年皮肌炎的观察性研究来说明方法。结果：使用传统方法拟合治疗概率模型，在治疗和协变量之间留下了重要的关联。相应增大治疗概率模型，既减少了混杂，又改变了治疗效果的估计。结论：治疗概率模型的传统模型拟合策略可能会在重新加权的数据集中留下治疗和协变量之间的重要关联。本文中描述的框架可以帮助检测这种情况，并制定更好的治疗概率模型。(c) 2008爱思唯尔公司版权所有。
Objectives: Marginal structural models (MSMs) are an increasingly popular framework in which observational data are reweighted to draw causal inferences. The ability of an MSM to accomplish this rests crucially on treatment being unconfounded by covariates in the inverse probability-of-treatment weighted data set. This paper discusses how this important property of the weights can be evaluated when both the treatment and the covariates are time dependent. Often treatment effect estimates will be sensitive to the choice of weights. A framework within which to explore reasons for this sensitivity and to select a good weighting scheme is suggested.Study Design and Setting: The methods are illustrated using an observational study of intravenous immunoglobulin for the treatment Of juvenile dermatomyositis.Results: Using traditional methods for fitting the probability-of-treatment model leaves important associations between treatment and covariates. Augmenting the probability-of-treatment model accordingly both reduces the confounding and alters the treatment effect estimate.Conclusions: Traditional model-fitting strategies for the probability-of-treatment model may leave important associations between treatment and covariates in the reweighted data set. The framework described in this article can help both to detect this and to formulate a better probability-of-treatment model. (c) 2008 Elsevier Inc. All rights reserved.