Methods for adjusting for covariate measurement error in flexible modelling of functional form: designing a blinded, controlled neutral comparison simulation study
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函数形式灵活建模中协变量测量误差校正方法：一项盲法、受控的中立比较模拟研究设计
- 研究问题
- 摘要称，研究关注在连续且含测量误差的暴露变量与二值结局之间函数关系的学习中，如何比较测量误差校正方法与灵活回归建模技术的组合表现。
- 方法与思路
- 作者提出一项中立比较模拟研究，由四个独立团队分工：一队负责数据生成与评估，另三队分别实现模拟-外推、回归校准、多重插补与贝叶斯校正方法；分三阶段推进，并在第一阶段对 B 样条、P 样条与分数多项式等灵活建模实现进行标准化。
- 材料陈述的贡献
- 摘要称，该工作强调中立比较研究对公平评价应对复杂分析挑战的统计方法的意义，并通过大型协作项目展示其可行性；三阶段设计包含对第二阶段 150 个情景的独立重复以量化估计的抽样方差。
- 阅读关注点
- 应核对各校正方法与灵活建模组合的具体设定、盲法如何维持、评估指标与评判标准、情景参数变化范围，以及第三阶段重复次数与抽样方差量化的具体做法。
- 阅读边界
- 摘要未说明具体数值结果、方法优劣排序、识别假设与理论性质；该文为 arXiv 预印本，同行评审与期刊接收情况未经核实，不能据此声称已获评审通过。
原文摘要
This article describes the design of a neutral comparison study in the context of empirical studies where the interest is in learning the functional relationship between a continuous errorprone exposure variable and a binary outcome. The performance of combinations of measurement error correction methods and flexible regression modeling techniques was compared using a simulation study. The project involved four independent teams, one devoted to data generation and evaluation, the other three to specific methods of measurement error correction (Simulation-Extrapolation, Regression-Calibration and Multiple imputation, Bayesian method). The study was conducted in three successive stages. In Stage 1, the first team simulated five datasets differing only by the true exposure-outcome functional form and distribution of true exposure. Furthermore, the implementation of flexible modeling methods (B-splines, P-splines, and fractional polynomials) was standardized. The three methods teams, blinded to the underlying data generation process, created the codes to implement their methods, and provided their results to the first team who evaluated them. These codes were then used by this team in the next Stages of the project. In Stage 2, the team simulated 150 additional datasets where other design parameters varied while using the same five exposureoutcome functions. Stage 3 consisted of simulating independent replications of each of the 150 scenarios considered in Stage 2 to quantify the sampling variance of the estimates. This work emphasizes the relevance of neutral comparison studies to fairly evaluate statistical methods aimed at addressing a complex analytical challenge, and demonstrates their feasibility through a large collaborative project.