Chattopadhyay Ambarish, Hase Christopher H, Zubizarreta José R
Department of Statistics, Harvard University, Cambridge, Massachusetts, USA.
Department of Health Care Policy, Harvard Medical School, Harvard University, Boston, Massachusetts, USA.
Stat Med. 2020 Oct 30;39(24):3227-3254. doi: 10.1002/sim.8659. Epub 2020 Sep 3.
There are two seemingly unrelated approaches to weighting in observational studies. One of them maximizes the fit of a model for treatment assignment to then derive weights-we call this the modeling approach. The other directly optimizes certain features of the weights-we call this the balancing approach. The implementations of these two approaches are related: the balancing approach implicitly models the propensity score, while instances of the modeling approach impose balance conditions on the covariates used to estimate the propensity score. In this article, we review and compare these two approaches to weighting. Previous review papers have focused on the modeling approach, emphasizing the importance of checking covariate balance. However, as we discuss, the dispersion of the weights is another important aspect of the weights to consider, in addition to the representativeness of the weighted sample and the sample boundedness of the weighted estimator. In particular, the dispersion of the weights is important because it translates into a measure of effective sample size, which can be used to select between alternative weighting schemes. In this article, we examine the balancing approach to weighting, discuss recent methodological developments, and compare instances of the balancing and modeling approaches in a simulation study and an empirical study. In practice, unless the treatment assignment model is known, we recommend using the balancing approach to weighting, as it systematically results in better covariate balance with weights that are minimally dispersed. As a result, effect estimates tend to be more accurate and stable.
在观察性研究中，有两种看似不相关的加权方法。其中一种方法是使治疗分配模型的拟合度最大化，进而得出权重——我们称之为建模方法。另一种方法则直接优化权重的某些特征——我们称之为平衡方法。这两种方法的实现方式是相关的：平衡方法隐含地对倾向得分进行建模，而建模方法的实例则对用于估计倾向得分的协变量施加平衡条件。在本文中，我们回顾并比较这两种加权方法。以往的综述文章主要关注建模方法，强调检查协变量平衡的重要性。然而，正如我们所讨论的，除了加权样本的代表性和加权估计量的样本有界性之外，权重的离散度是权重需要考虑的另一个重要方面。特别是，权重的离散度很重要，因为它转化为有效样本量的一种度量，可用于在不同的加权方案之间进行选择。在本文中，我们研究加权的平衡方法，讨论最近的方法学进展，并在模拟研究和实证研究中比较平衡方法和建模方法的实例。在实际应用中，除非治疗分配模型已知，我们建议使用平衡方法进行加权，因为它系统地导致更好的协变量平衡，且权重的离散度最小。因此，效应估计往往更准确、更稳定。
