Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-event endpoints. We target an average-treatment-effect-on-the-treated (ATT)-type marginal hazard-ratio estimand, comparing treatment with counterfactual control in the treated trial population, and estimate it using inverse-probability-weighted (IPW) Cox regression. Valid inference is challenging because IPW Cox regression depends on the weights through both event contributions and risk-set averages, making flexible machine-learning nuisance estimation difficult to incorporate directly. Building on machine-learning-assisted generalized entropy calibration (MEC) by Lee and Kim (2026), we propose MEC-Cox for ATT-weighted IPW Cox regression. The method begins with normalized source-propensity-score odds weights for external controls and then applies Bregman calibration to balance cross-fitted prognostic summaries between external controls and treated trial patients. The calibration basis may include control-survival predictions, Cox linear predictors, penalized-survival-model predictions, or other prognostic-score summaries. MEC-updated weights therefore play a dual role as source-transport and prognostic-score balancing weights. We establish consistency, characterize a calibration-induced efficiency gain, and develop a stacked sandwich variance estimator. Simulations show that MEC-Cox can reduce bias, increase efficiency, and improve coverage through flexible machine-learning-assisted adjustment.

翻译：当同期随机对照不可行时，外部对照生存试验愈发被采用，尤其在肿瘤学和罕见病领域中带有时间至事件结局的研究中。本研究以处理组平均处理效应（ATT）型边际风险比为目标估计量，比较治疗组人群中的实际治疗效应与反事实对照效应，并通过逆概率加权Cox回归进行估计。有效推断具有挑战性，因为逆概率加权Cox回归对权重的依赖同时体现在事件贡献和风险集平均值上，使得灵活的机器学习干扰估计难以直接纳入。基于Lee和Kim（2026）提出的机器学习辅助广义熵校准（MEC）方法，我们提出适用于ATT加权逆概率加权Cox回归的MEC-Cox方法。该方法首先对外部对照计算归一化的源倾向评分比值权重，然后应用Bregman校准，以平衡外部对照和治疗组患者之间交叉拟合的预后摘要。校准基础可包括对照生存预测、Cox线性预测因子、惩罚生存模型预测或其他预后评分摘要。因此，MEC更新后的权重同时扮演源转移权重和预后评分平衡权重的双重角色。我们建立了相合性，刻画了校准引发的效率增益，并开发了堆叠夹层方差估计器。模拟结果表明，MEC-Cox通过灵活的机器学习辅助调整，能够减少偏差、提高效率并改善覆盖度。