High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates
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部分缺失混杂变量的高维多重插补：纳入自然语言处理辅助变量
- 研究问题
- 当混杂变量部分缺失且缺失依赖未观测因素时，结构化及文本辅助变量能否改善多重插补后的治疗效应估计？
- 方法与思路
- 作者开展基于真实数据的 plasmode 模拟，以 LASSO 筛选结构化和文本辅助变量，完成多重插补后通过倾向得分匹配估计效应。
- 材料陈述的贡献
- 摘要称，理赔数据辅助插补的偏差最低；结合句子嵌入时均方根误差最低、覆盖率为 94%，仅文本变量未优于基线。
- 阅读关注点
- 应核对缺失机制及未观测混杂的模拟方式、辅助变量筛选是否涉及不可用信息，以及插补、匹配和不确定性合并流程。
- 阅读边界
- 仅据 arXiv 预印本摘要解读，同行评审及期刊接收未经核实；结论来自特定模拟，不能据此认定一般非随机缺失可识别。
原文摘要
Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.