Department of Statistics, University of Auckland, Auckland, New Zealand.
Center on Methods for Implementation and Dissemination Science, Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut.
Stat Med. 2019 Oct 15;38(23):4611-4624. doi: 10.1002/sim.8321. Epub 2019 Jul 29.
In public health research, information that is readily available may be insufficient to address the primary question(s) of interest. One cost-efficient way forward, especially in resource-limited settings, is to conduct a two-phase study in which the population is initially stratified, at phase I, by the outcome and/or some categorical risk factor(s). At phase II detailed covariate data is ascertained on a subsample within each phase I strata. While analysis methods for two-phase designs are well established, they have focused exclusively on settings in which participants are assumed to be independent. As such, when participants are naturally clustered (eg, patients within clinics) these methods may yield invalid inference. To address this, we develop a novel analysis approach based on inverse-probability weighting that permits researchers to specify some working covariance structure and appropriately accounts for the sampling design and ensures valid inference via a robust sandwich estimator for which a closed-form expression is provided. To enhance statistical efficiency, we propose a calibrated inverse-probability weighting estimator that makes use of information available at phase I but not used in the design. In addition to describing the technique, practical guidance is provided for the cluster-correlated data settings that we consider. A comprehensive simulation study is conducted to evaluate small-sample operating characteristics, including the impact of using naïve methods that ignore correlation due to clustering, as well as to investigate design considerations. Finally, the methods are illustrated using data from a one-time survey of the national antiretroviral treatment program in Malawi.
在公共卫生研究中，现成的信息可能不足以解决主要关注的问题。一种具有成本效益的方法是进行两阶段研究，即在第一阶段根据结果和/或某些分类风险因素对人群进行分层；在第二阶段，在每个第一阶段分层的子样本中确定详细的协变量数据。虽然两阶段设计的分析方法已经很成熟，但它们仅专注于假设参与者相互独立的环境。因此，当参与者自然聚类（例如，诊所内的患者）时，这些方法可能会产生无效的推断。为了解决这个问题，我们开发了一种基于逆概率加权的新分析方法，允许研究人员指定一些工作协方差结构，并适当考虑抽样设计，通过稳健的三明治估计器确保有效推断，提供了封闭形式的表达式。为了提高统计效率，我们提出了一种校准的逆概率加权估计器，它利用了第一阶段的信息，但未用于设计。除了描述技术外，还为我们考虑的聚类相关数据环境提供了实用指南。进行了全面的模拟研究，以评估小样本的操作特征，包括使用由于聚类而忽略相关性的简单方法的影响，以及研究设计考虑因素。最后，使用马拉维一次性全国抗逆转录病毒治疗方案调查的数据说明了这些方法。
