ESTIMATING EXPOSURE EFFECTS BY MODELING THE EXPECTATION OF EXPOSURE CONDITIONAL ON CONFOUNDERS
ESTIMATING EXPOSURE EFFECTS BY MODELING THE EXPECTATION OF EXPOSURE CONDITIONAL ON CONFOUNDERS
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DOI：
10.2307/2532304
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发表时间：
1992-06-01
期刊：
影响因子：
1.9
通讯作者：
NEWEY, WK
中科院分区：
文献类型：
--
作者：
ROBINS, JM;MARK, SD;NEWEY, WK
为了估计一个或多个暴露或治疗对感兴趣的结果的因果效应，必须考虑“混杂因素”的影响，这些因素既与暴露或治疗协变，又是结果的独立预测因子。在本文中，我们提出了回归方法，在标准方法，调整多个连续或离散协变量的混杂效应，建模的条件期望的暴露或治疗的混杂因素。在单变量二分暴露或治疗的特殊情况下，这种条件期望与罗森鲍姆和鲁宾所称的倾向得分相同。他们还提出了通过对倾向得分进行建模来估计因果效应的方法。我们的方法在几个方面推广了Rosenbaum和Rubin的方法。首先，我们的方法直接允许多变量暴露或治疗，其中每一个可以是连续的，有序的，或离散的。其次，即使在单一二分暴露的情况下，我们的方法也不需要对倾向评分进行子分类或匹配，因此“残余混杂”的可能性，即，避免了由于不完全匹配而导致的偏差。第三，我们的方法允许一个相当一般的形式化的想法，它是更好地使用“估计的倾向得分”比真实的倾向得分，即使真实的分数是已知的。我们的方法的额外力量来自于这样一个事实，即我们假设暴露或治疗的因果效应可以通过半参数回归模型的参数分量来描述。为了说明我们的方法，我们重新分析了当前吸烟对2,713名成年白色男性一秒钟用力呼气量水平的影响。我们将结果与使用标准方法获得的结果进行比较。
In order to estimate the causal effects of one or more exposures or treatments on an outcome of interest, one has to account for the effect of "confounding factors" which both covary with the exposures or treatments and are independent predictors of the outcome. In this paper we present regression methods which, in contrast to standard methods, adjust for the confounding effect of multiple continuous or discrete covariates by modelling the conditional expectation of the exposures or treatments given the confounders. In the special case of a univariate dichotomous exposure or treatment, this conditional expectation is identical to what Rosenbaum and Rubin have called the propensity score. They have also proposed methods to estimate causal effects by modelling the propensity score. Our methods generalize those of Rosenbaum and Rubin in several ways. First, our approach straightforwardly allows for multivariate exposures or treatments, each of which may be continuous, ordinal, or discrete. Second, even in the case of a single dichotomous exposure, our approach does not require subclassification or matching on the propensity score so that the potential for "residual confounding," i.e., bias, due to incomplete matching is avoided. Third, our approach allows a rather general formalization of the idea that it is better to use the "estimated propensity score" than the true propensity score even when the true score is known. The additional power of our approach derives from the fact that we assume the causal effects of the exposures or treatments can be described by the parametric component of a semiparametric regression model. To illustrate our methods, we reanalyze the effect of current cigarette smoking on the level of forced expiratory volume in one second in a cohort of 2,713 adult white males. We compare the results with those obtained using standard methods.