Propensity score analysis for a semi-continuous exposure variable: a study of gestational alcohol exposure and childhood cognition.
Propensity score analysis for a semi-continuous exposure variable: a study of gestational alcohol exposure and childhood cognition.
复制标题
半连续暴露变量的倾向评分分析：妊娠期酒精暴露和儿童认知的研究。
DOI：
10.1111/rssa.12716
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Ryan,LouiseM
中科院分区：
文献类型：
--
作者：
Hocagil,TugbaAkkaya;Cook,RichardJ;Jacobson,SandraW;Jacobson,JosephL;Ryan,LouiseM
近年来，倾向评分法作为一种估计观察性研究中因果效应的工具越来越受欢迎。许多相关的研究一直针对设置与二进制或离散的暴露变量与最近的工作涉及连续的暴露变量。在环境流行病学中，很大一部分人通常完全没有接触，而其他人可能经历了严重的接触，导致接触分布的点质量为零，右尾较重。我们提出了一种新的方法来处理这种类型的暴露数据，通过构建一个两部分模型的基础上的倾向得分，并显示如何使用该模型可以更可靠地调整协变量的半连续暴露变量。我们还考虑的情况下，当一个错误指定的倾向得分被用于回归调整，并得出一个明确的形式的偏见。我们发现，潜在的偏差变得更小，估计的倾向得分得到更接近真实的期望的暴露变量给定一组观察到的协变量。虽然这一结果涉及到一个更一般的设置，我们用它来评估设置中的潜在偏差，其中真正的曝光具有半连续结构。我们还评估和比较了我们提出的方法的性能，通过模拟研究相对于一个简单的线性回归为基础的倾向得分的连续暴露变量，以及通过直接协变量调整。总体而言，我们发现，使用通过两部分模型构建的倾向评分显着提高了回归估计时，暴露变量是半连续的性质。特别是当未暴露受试者的比例很高，协变量对暴露和结局的影响很强时，建议的两部分倾向评分法优于更标准的竞争方法。我们使用底特律纵向队列研究的数据来说明我们的方法，其中暴露变量反映了妊娠期酒精暴露的零值和长尾。
Propensity score methodology has become increasingly popular in recent years as a tool for estimating causal effects in observational studies. Much of the related research has been directed at settings with binary or discrete exposure variables with more recent work involving continuous exposure variables. In environmental epidemiology, a substantial proportion of individuals is often completely unexposed while others may experience heavy exposure leading to an exposure distribution with a point mass at zero and a heavy right tail. We suggest a new approach to handle this type of exposure data by constructing a propensity score based on a two-part model and show how this model can be used to more reliably adjust for covariates of a semi-continuous exposure variable. We also consider the case when a misspecified propensity score is used in a regression adjustment and derive an explicit form of the bias. We show that the potential bias gets smaller as the estimated propensity score gets closer to the true expectation of the exposure variable given a set of observed covariates. While this result pertains to a more general setting, we use it to evaluate the potential bias in settings in which the true exposure has a semi-continuous structure. We also evaluate and compare the performance of our proposed method through simulation studies relative to a simpler linear regression-based propensity score for a continuous exposure variable as well as through direct covariate adjustment. Overall, we find that using a propensity score constructed via a two-part model significantly improves the regression estimate when the exposure variable is semi-continuous in nature. Specifically when the proportion of non-exposed subjects is high and the effects of covariates on exposure and outcome are strong, the proposed two-part propensity score method outperforms the more standard competing methods. We illustrate our method using data from the Detroit Longitudinal Cohort Study in which the exposure variable reflects gestational alcohol exposure featuring zero values and a long tail.