AI 中文总结
本文提出边际广义raking方法，将广义raking的影响函数理论推广到边际估计量，并通过全合成模拟和HIV队列数据应用，与朴素边际化条件GR估计量的方法作比较。
AI 中文摘要
广义raking（GR）最初在调查统计文献中被提出，用于在估计中纳入辅助信息。近来它被用于生物统计和流行病学文献中，以估计存在缺失数据（包括设计性缺失，如两阶段研究）时参数模型中的回归系数。在回归参数语境下，最优GR估计量已被证明等价于最优增广逆概率加权估计量。本文将GR的基于影响函数的理论推广到边际估计量，将所提方法称为边际广义raking。我们在全合成模拟及HIV感染者观察性队列数据的应用中，将该方法与对回归参数的条件GR估计量进行边际化的朴素程序进行比较。
英文摘要
Generalized raking (GR) was originally developed in the survey statistics literature to incorporate auxiliary information in estimation. Recently, it has been used in the biostatistical and epidemiological literature to estimate regression coefficients in parametric models in cases with missing data, including missing data by design (e.g., two-phase studies). In the regression parameter context, the optimal GR estimator has been shown to be equivalent to the optimal augmented inverse probability weighted estimator. In this paper, we generalize the influence function-based theory for GR to marginal estimands; we call our approach \textit{marginal generalized raking}. We compare our approach to a naive procedure that marginalizes a conditional GR estimator of regression parameters in both fully-synthetic simulations and in an application using data from an observational cohort of persons living with HIV.
Comments26 pages, 6 tables (main); 31 pages, 18 tables (supplementary)
