Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies
Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies
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DOI：
10.1191/0962280204sm351ra
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发表时间：
2004-02-01
影响因子：
2.3
通讯作者：
Lancaster, T
中科院分区：
文献类型：
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作者：
Hogan, JW;Lancaster, T
从纵向重复测量数据推断因果效应与许多研究领域高度相关，包括经济学、社会科学和流行病学。特别是在观察性研究中，治疗接受机制通常不受研究者控制;它可能取决于各种因素，包括关注的结局。这会导致不同的选择到治疗水平，并可能导致选择偏差时，标准的程序，如最小二乘回归被用来估计因果effects.Interestingly，无论是表征和方法处理选择偏差可以有很大的不同学科的传统。在社会科学和经济学中，工具变量（IV）是估计线性和非线性模型的标准方法，其中误差项可能与观察到的协变量相关。当不排除这种相关性时，协变量被称为内生变量，协变量效应的最小二乘估计值通常存在偏倚。工具变量的可用性可用于减少或消除偏倚。在公共卫生和临床医学（例如，流行病学和生物统计学），选择偏差通常根据混杂因素来看待，并且流行的方法适于通过明确使用观察到的混杂因素来进行适当的调整（例如，分层、标准化）。一类方法称为逆概率加权（IPW）估计，它依赖于建模选择的混杂因素，越来越受欢迎，使这样的adjustment. We的目标是审查和比较IPW和IV估计因果治疗效果的纵向数据，治疗可能会随着时间的推移。我们通过定义潜在结果（反事实）的线性随机模型的因果被估量来实现这一点。我们的比较包括对因果推理讨论中通常使用的术语的回顾（例如，混杂，内分泌）;审查确定因果效应及其对估计和解释的影响所需的假设;通过反加权和工具变量描述估计;对艾滋病毒感染妇女纵向队列研究的数据进行比较分析。在我们对假设和估计例程的讨论中，我们试图强调实现相对标准的分析所需的充分条件，这些分析基本上可以用回归模型来表示。从这个意义上说，这篇综述是面向定量practitioner.The数据分析的目的是估计因果（治疗）的影响，接受联合抗病毒治疗的纵向CD 4细胞计数，其中接受治疗随时间而变化，并取决于CD 4计数和其他协变量。假设审查的背景下，并由此产生的推论进行比较。分析说明了考虑不可测混杂和检查“弱仪器”的重要性。“这也表明，IV方法可能在纵向队列研究中发挥作用，其中潜在的工具变量是可用的。
Inferring causal effects from longitudinal repeated measures data has high relevance to a number of areas of research, including economics, social sciences and epidemiology. In observational studies in particular, the treatment receipt mechanism is typically not under the control of the investigator; it can depend on various factors, including the outcome of interest. This results in differential selection into treatment levels, and can lead to selection bias when standard routines such as least squares regression are used to estimate causal effects.Interestingly, both the characterization of and methodology for handling selection bias can differ substantially by disciplinary tradition. In social sciences and economics, instrumental variables (IV) is the standard method for estimating linear and nonlinear models in which the error term may be correlated with an observed covariate. When such correlation is not ruled out, the covariate is called endogenous and least squares estimates of the covariate effect are typically biased. The availability of an instrumental variable can be used to reduce or eliminate the bias.In public health and clinical medicine (e.g., epidemiology and biostatistics), selection bias is typically viewed in terms of confounders, and the prevailing methods are geared toward making proper adjustments via explicit use of observed confounders (e.g., stratification, standardization). A class of methods known as inverse probability weighting (IPW) estimators, which relies on modeling selection in terms of confounders, is gaining in popularity for making such adjustments.Our objective is to review and compare IPW and IV for estimating causal treatment effects from longitudinal data, where the treatment may vary with time. We accomplish this by defining the causal estimands in terms of a linear stochastic model of potential outcomes (counterfactuals). Our comparison includes a review of terminology typically used in discussions of causal inference (e.g., confounding, endogeneity); a review of assumptions required to identify causal effects and their implications for estimation and interpretation; description of estimation via inverse weighting and instrumental variables; and a comparative analysis of data from a longitudinal cohort study of HIV-infected women. In our discussion of assumptions and estimation routines, we try to emphasize sufficient conditions needed to implement relatively standard analyses that can essentially be formulated as regression models. In that sense this review is geared toward the quantitative practitioner.The objective of the data analysis is to estimate the causal (therapeutic) effect of receiving combination antiviral therapy on longitudinal CD4 cell counts, where receipt of therapy varies with time and depends on CD4 count and other covariates. Assumptions are reviewed in context, and resulting inferences are compared. The analysis illustrates the importance of considering the existence of unmeasured confounding and of checking for 'weak instruments.' It also suggests that IV methodology may have a role in longitudinal cohort studies where potential instrumental variables are available.