Proportional hazard Cox regression models are frequently used to analyze the impact of different factors on time-to-event outcomes. Most practitioners are familiar with and interpret research results in terms of hazard ratios. Direct differences in survival curves are, however, easier to understand for the general population of users and to visualize graphically. Analyzing the difference among the survival curves for the population at risk allows easy interpretation of the impact of a therapy over the follow-up. When the available information is obtained from observational studies, the observed results are potentially subject to a plethora of measured and unmeasured confounders. Although there are procedures to adjust survival curves for measured covariates, the case of unmeasured confounders has not yet been considered in the literature. In this article we provide a semi-parametric procedure for adjusting survival curves for measured and unmeasured confounders. The method augments our novel instrumental variable estimation method for survival time data in the presence of unmeasured confounding with a procedure for mapping estimates onto the survival probability and the expected survival time scales.

中文翻译：

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在存在无法测量的混杂因素的情况下总结生存曲线的因果差异

比例风险 Cox 回归模型经常用于分析不同因素对事件发生时间结果的影响。大多数从业者都熟悉并根据风险比来解释研究结果。然而，对于一般用户群体来说，生存曲线的直接差异更容易理解并以图形方式可视化。分析高危人群的生存曲线之间的差异，可以轻松解释治疗对随访的影响。当从观察性研究中获得可用信息时，观察到的结果可能会受到大量测量和未测量的混杂因素的影响。尽管有一些程序可以调整测量协变量的生存曲线，但文献中尚未考虑未测量混杂因素的情况。在本文中，我们提供了一个半参数程序，用于调整已测量和未测量混杂因素的生存曲线。该方法通过将估计映射到生存概率和预期生存时间尺度的程序，在存在未测量混杂的情况下增强了我们用于生存时间数据的新型工具变量估计方法。