In some clinical studies patient survival beyond a specific point in time, $t_0$, say, may be of special interest as it may for instance indicate patient cure. To analyze the $t_0$-year risk for such patients may be accomplished using logistic regression with appropriate weights (IPWCC) that may further be augmented (AIPWCC) to improve efficiency. In this paper, we derive the most efficient estimator for this problem, which is different from the AIPWCC based on the full data efficient influence function. We first give the result for a survival endpoint and then generalize to the competing risk setting. The proposed estimators superior behavior is illustrated using simulations as well as applying it to some real data concerning the survival of blood and marrow transplanted patients.

中文翻译：

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使用逻辑模型进行高效的 t0$$ {t}_0 $$ 年风险回归

在一些临床研究中，患者的存活率超过了特定时间点，$t_0$，比如说，可能会特别令人感兴趣，因为它可能表明患者已治愈。来分析$t_0$此类患者的年风险可以使用具有适当权重的逻辑回归（IPWCC）来完成，该权重可以进一步增强（AIPWCC）以提高效率。在本文中，我们针对该问题推导了最有效的估计器，这与基于全数据有效影响函数的AIPWCC不同。我们首先给出生存终点的结果，然后推广到竞争风险设置。通过模拟以及将其应用于有关血液和骨髓移植患者存活的一些真实数据来说明所提出的估计器的优越行为。