论文标题
使用流行病学队列的有效且稳健的倾向得分方法用于人群推断
Efficient and Robust Propensity-Score-Based Methods for Population Inference using Epidemiologic Cohorts
论文作者
论文摘要
大多数流行病学人群都是由不代表一般人群的志愿者组成的。为了使人群推论，我们和其他人提出了利用概率调查样本作为外部参考的倾向评分（PS），以构成队列与调查中成员的倾向评分（PS）。本文中，我们为基于PS的加权（例如逆PS加权（IPSW））和匹配方法（例如内核加权（KW）方法（KW）方法）开发了一个统一的框架。我们确定了基于现有的基于PS的匹配方法的基本强大交换性假设（SEA），即使正确指定了PS模型，其失败也无效推理。我们为匹配方法放松海洋为弱的交换性假设（WEA）。此外，我们提出了IPSW和KW.S方法，通过缩放PS估计中使用的调查权重来减少基于PS的估计值的方差。我们证明了IPSW和KW.S人口平均值和WEA中的流行率的一致性，并提供渐近方差和一致的方差估计器。在模拟中，KW.S和IPSW估计器的MSE最小。在我们的数据示例中，原始KW估计值较大，而KW.S估计值最小。
Most epidemiologic cohorts are composed of volunteers who do not represent the general population. To enable population inference from cohorts, we and others have proposed utilizing probability survey samples as external references to develop a propensity score (PS) for membership in the cohort versus survey. Herein we develop a unified framework for PS-based weighting (such as inverse PS weighting (IPSW)) and matching methods (such as kernel-weighting (KW) method). We identify a fundamental Strong Exchangeability Assumption (SEA) underlying existing PS-based matching methods whose failure invalidates inference even if the PS-model is correctly specified. We relax the SEA to a Weak Exchangeability Assumption (WEA) for the matching method. Also, we propose IPSW.S and KW.S methods that reduce the variance of PS-based estimators by scaling the survey weights used in the PS estimation. We prove consistency of the IPSW.S and KW.S estimators of population means and prevalences under WEA, and provide asymptotic variances and consistent variance estimators. In simulations, the KW.S and IPSW.S estimators had smallest MSE. In our data example, the original KW estimates had large bias, whereas the KW.S estimates had the smallest MSE.
