数据驱动亚组发现后标准试验估计量的推断
Inference for Standard Trial Estimands after Data-Driven Subgroup Discovery
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中文总结 AI 辅助
针对数据驱动亚组发现后的选择偏差，提出一个过程无关的后选择推断框架，以条件自适应估计量为目标，通过乘子重采样和无穷小刀切区间改善覆盖率。
中文摘要 AI 辅助
数据驱动的亚组识别在临床试验中日益普及，然而针对所发现亚组报告的效果通常是标准分析：在该亚组内拟合的Cox或广义线性模型治疗系数。通过效果驱动规则（在筛选和规模标准下奖励大估计效果）进行选择会夸大所报告的系数：这种赢家诅咒使得朴素区间失效，无论识别器内部估计量是否通过交叉拟合去偏。我们开发了一个与过程无关的后选择推断框架，将发现与报告分离：森林搜索、自然参数差估计器和因果森林生成可解释的候选亚组；选择与待报告系数对齐；目标是所选亚组的标准分析效果，候选族固定不变——这是一个条件性的、数据自适应的估计量。随后对搜索进行重采样简化为联合扰动所有候选效果，产生无需重拟合的乘子重采样，并带有无穷小刀切区间，我们在标准正则性和关于极限竞争的显式条件下刻画了其覆盖率。它在固定族上与完全自助法匹配；在模型生成的族上，它对相同的条件目标保持有效，而自助法仅在进一步条件下才能复现该目标。两项试验应用和匹配模拟显示选择偏差得到缓解且覆盖率得到改善。
英文摘要
Data-driven subgroup identification is increasingly used in clinical trials, yet the effect reported for a discovered subgroup is typically the standard analysis: a Cox or generalized-linear-model treatment coefficient fitted within that subgroup. Selection by an effect-driven rule --- rewarding large estimated effects subject to screening and size criteria --- inflates the reported coefficient: a winner's curse invalidating naive intervals whether or not the identifier's internal estimand was de-biased by cross-fitting. We develop a procedure-agnostic framework for post-selection inference that separates discovery from reporting: forest search, difference-in-natural-parameters estimators, and causal forests generate interpretable candidate subgroups; selection is aligned with the coefficient to be reported; and the target is the standard-analysis effect of the selected subgroup, the candidate family held fixed --- a conditional, data-adaptive estimand. Resampling the search then reduces to jointly perturbing all candidate effects, yielding refit-free multiplier resampling with an infinitesimal-jackknife interval whose coverage we characterize under standard regularity and an explicit condition on the limiting competition. It matches the full bootstrap on a fixed family; on model-generated families it remains valid for the same conditional target, which the bootstrap reproduces only under further conditions. Two trial applications and matched simulations show mitigated selection bias and improved coverage.
