Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models.
Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models.
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动态和静态纵向边际结构工作模型的目标最大似然估计。
DOI：
10.1515/jci-2013-0007
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发表时间：
2014-06-18
影响因子：
1.4
通讯作者：
van der Laan M
中科院分区：
文献类型：
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作者：
Petersen M;Schwab J;Gruber S;Blaser N;Schomaker M;van der Laan M
本文介绍了纵向静态和动态边际结构模型参数的目标极大似然估计（TMLE）。我们考虑一个纵向数据结构，包括基线协变量，时间依赖性干预节点，中间时间依赖性协变量，和可能的时间依赖性的结果。每个时间点的干预节点可以包括二元治疗以及右删失指标。给定一类动态或静态干预措施，使用边际结构模型将干预措施特异性反事实结局的平均值建模为干预措施、时间点和可能的基线协变量子集的函数。因为这个函数的真实形状很少被人知道，所以边际结构模型被用作工作模型。感兴趣的因果量被定义为真实函数在该工作模型上的投影。Robins（2000，2002）和Bang and Robins（2005）提出了边际结构模型参数的迭代条件期望双稳健估计。在这里，我们建立在这项工作，并提出了一个汇集TMLE的边际结构工作模型的参数。我们将该合并估计量与分层TMLE（Schnitzer et al. 2014）进行比较，分层TMLE基于分别估计每种感兴趣干预的干预特异性均值。合并TMLE的性能进行比较，分层TMLE的性能和逆概率加权（IPW）估计的性能，使用模拟。概念说明使用一个例子，其目的是估计延迟开关的因果关系的影响，免疫失败的第一线抗逆转录病毒治疗艾滋病毒感染患者。从国际流行病学数据库评估艾滋病，南部非洲的数据进行了分析，调查这个问题，使用TML和IPW估计。我们的研究结果表明，在IPW估计的工作边际结构模型的生存，以及在汇总TMLE是上级其分层对应的情况下，汇集TMLE的实际优势。
This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.