Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment
暴露或结局罕见时哪种正则化倾向得分与双稳健方法校准最佳？基于代理混杂调整的plasmode研究AI 解读 ↓
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暴露或结局罕见时哪种正则化倾向得分与双稳健方法校准最佳？基于代理混杂调整的plasmode研究
- 研究问题
- 摘要称，在医疗数据库研究中用大量代理变量做混杂调整、每变量事件数偏低时，不同正则化变量选择策略（OAL、GLiDeR、HAL等）与双稳健估计如何比较，尤其在暴露或结局罕见情形下哪种方法校准最好，此前研究较少。
- 方法与思路
- 作者提出一项以NHANES 2013-2018数据为锚的plasmode模拟，含25个研究者指定协变量与142个处方衍生代理；比较十种流程，结合IPTW与TMLE，在真实风险差为零的基准下设频繁、罕见暴露、罕见结局三种情景，报告偏倚、标准误、相对误差、95%覆盖率和运行时间。
- 材料陈述的贡献
- 摘要称，在零效应基准下，将结局感知的变量选择（OAL、GLiDeR）或双稳健估计（TMLE）与正则化模型配对，能最好地平衡偏倚、校准与对罕见性的稳健性；罕见暴露情景暴露的差距最大；方法选择需在优先指标与计算成本间权衡。
- 阅读关注点
- 阅读全文时应核对：plasmode模拟的具体生成机制与零效应锚定方式、十种流程的精确定义与调参、罕见暴露与罕见结局的阈值设定、覆盖率与相对误差的计算细节、运行时间比较的硬件与实现条件，以及真实数据结果是否支持模拟结论。
- 阅读边界
- 仅依据摘要，无法判断模拟设计的外部有效性、各方法在非零效应下的表现、实现细节与统计推断的可靠性；摘要未说明代理变量测量误差结构、缺失数据处理及敏感性分析。该文为arXiv预印本，未经同行评审或期刊接收验证。
原文摘要
Purpose. Confounding adjustment in health-care database studies screens large proxy libraries where events per variable are low, straining standard propensity score (PS) methods, and many regularized variable-selection strategies exist (outcome-adaptive LASSO [OAL], group LASSO/GLiDeR, highly adaptive LASSO [HAL]). Yet few comparisons have varied exposure prevalence within such a selection menu, and none pairs it with doubly robust estimation, compute accounting, and a null-RD truth anchor. Methods. We conducted a plasmode simulation anchored on National Health and Nutrition Examination Survey data (2013-2018; 25 investigator-specified covariates, 142 prescription-derived proxies), comparing ten pipelines combining these strategies with inverse probability of treatment weighting (IPTW) and targeted maximum likelihood estimation (TMLE). Three scenarios were evaluated under a known null (true risk difference, RD = 0): frequent, rare-exposure, and rare-outcome. We report bias, standard error (SE), relative error, 95% coverage, and runtime. Results. HAL (G-Computation) had near-zero bias but highly concentrated estimates, giving near-unity coverage and large relative error (106-186%). OAL (IPTW), GLiDeR, and HAL (TMLE) were best calibrated, whereas the regularized-LASSO TMLE pipelines under-covered modestly (91-93%) in the rare scenarios. Under rare exposure, LASSO-IPTW had the largest bias and inflated SE and over-covered (conservatively), problems that TMLE removed. On real data, methods agreed (RD approximately 0.07-0.085). Runtimes spanned 16 h. Conclusions. Under a null benchmark, pairing outcome-aware selection (OAL, GLiDeR) or doubly robust estimation (TMLE) with regularized models best balanced bias, calibration, and robustness to rarity. The rare-exposure arm exposed the largest gaps; method choice should weigh the prioritized metric against compute.