标题： 三相广义耙取和多重插补估计量以解决有误差的数据 显示英文标题

标题： Three-phase generalized raking and multiple imputation estimators to address error-prone data

摘要： 验证研究常用于在存在错误数据的环境中获取更可靠的信息。 对受试者子样本的验证数据可以与所有受试者的错误数据一起使用，以提高估计效果。 在实际操作中，可能需要多轮数据验证，直接应用标准方法将验证数据结合到分析中可能导致估计量效率低下，因为从中间验证步骤中获得的信息仅被部分考虑甚至完全忽略。 本文中，我们提出了多重插补和广义校准估计量的两种新扩展方法，充分利用所有可用数据。 我们通过模拟证明，结合中间步骤的信息可以显著提高效率。 这项工作受到一项研究的启发，并在一项针对82,957名感染HIV的女性避孕有效性研究中进行了说明，这些数据最初是从电子医疗记录中提取的，其中4855人的病历被审查，随后又有1203人接受了电话访谈以验证关键研究变量。 显示英文摘要

摘要： Validation studies are often used to obtain more reliable information in settings with error-prone data. Validated data on a subsample of subjects can be used together with error-prone data on all subjects to improve estimation. In practice, more than one round of data validation may be required, and direct application of standard approaches for combining validation data into analyses may lead to inefficient estimators since the information available from intermediate validation steps is only partially considered or even completely ignored. In this paper, we present two novel extensions of multiple imputation and generalized raking estimators that make full use of all available data. We show through simulations that incorporating information from intermediate steps can lead to substantial gains in efficiency. This work is motivated by and illustrated in a study of contraceptive effectiveness among 82,957 women living with HIV whose data were originally extracted from electronic medical records, of whom 4855 had their charts reviewed, and a subsequent 1203 also had a telephone interview to validate key study variables.