AI 中文总结
以自适应靶向最大似然估计为例，开发三个工具。包括使估计器偏差模型可审计的成绩单、对比仅试验估计器的融合利弊图、对增益进行选择感知推断，用于评估现实世界数据与试验数据结合的效果。
AI 中文摘要
用现实世界数据增强随机对照试验有望提高效率，但给定融合能带来多少增益以及如何诚实地衡量不确定性鲜有描述。以自适应靶向最大似然估计为例，开发三个可重复工具，用于从试验和现实世界数据组合中获取可靠证据，并用三个公开融合示例展示其作用。
英文摘要
Augmenting a randomized controlled trial (RCT) with real-world data (RWD) promises greater efficiency, but how much a given fusion delivers, and how to attach honest uncertainty to that gain, are rarely characterized. Using adaptive targeted maximum likelihood estimation (A-TMLE) as a worked example of an estimator that learns a working model and then debiases it, we develop three reproducible tools for reliable evidence from combined trial and real-world data. First, a report card that makes the data-adaptively learned bias model auditable: on simulated data it measures how well the model recovers the true enrollment-effect surface and attributes the estimator's variance to its structural parts. Second, a map of when fusion helps versus hurts, benchmarked against a matched trial-only estimator; the efficiency gain is driven mainly by the magnitude of the real-world bias rather than its functional complexity (a dominance an exact population-oracle variance identity explains), it crosses break-even near a moderate bias and erodes as the trial grows, so the advantage is finite-sample, not super-efficiency. Third, selection-aware inference for the gain, treated as a data-adaptive estimand: the naive standard error undercovers, and among ten candidate standard errors only a block jackknife achieved consistently near- or above-nominal coverage, though conservatively. Across six fusions of three openly available trials (a biomedical HIV trial, a public-health trial, and a job-training trial), only one interval clears one, and only marginally; in the rest, fusion has not earned an efficiency claim over the RCT alone. On real data the toolkit therefore functions mainly as a guardrail: the learned-model dimension is a stress diagnostic, not a proxy for ground truth, and the block-jackknife interval decides whether fusion or the RCT-only analysis should be primary.
Comments42 pages, 4 figures, 13 tables; 48-page supplementary Web Appendix included as an ancillary file. Reproducible code: https://github.com/ehsanx/atmle-efficiency
