Martínez-Camblor Pablo, MacKenzie Todd A, Staiger Douglas O, Goodney Phillip P, O'Malley A James
Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Lebanon, New Hampshire, USA.
Int J Biostat. 2020 Sep 18;17(2):223-240. doi: 10.1515/ijb-2019-0146.
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.
比例风险Cox回归模型常用于分析不同因素对事件发生时间结局的影响。大多数从业者熟悉风险比并据此解释研究结果。然而，生存曲线的直接差异对于一般用户群体来说更容易理解，并且便于以图形方式直观呈现。分析处于风险中的人群生存曲线之间的差异，能够轻松解读在随访期间一种治疗方法的影响。当从观察性研究中获取可用信息时，观察到的结果可能会受到大量已测量和未测量混杂因素的影响。尽管有针对已测量协变量调整生存曲线的方法，但文献中尚未考虑未测量混杂因素的情况。在本文中，我们提供了一种半参数方法，用于针对已测量和未测量混杂因素调整生存曲线。该方法在存在未测量混杂因素的情况下，将我们用于生存时间数据的新型工具变量估计方法与一种将估计值映射到生存概率和预期生存时间尺度的方法相结合。
