论文标题
casebase：生存分析和事件率比较的替代框架
casebase: An Alternative Framework For Survival Analysis and Comparison of Event Rates
论文作者
论文摘要
在事件时间数据的流行病学研究中，临床医生和患者的兴趣数量是给定协变量的事件的风险。但是，依靠时间匹配或风险集采样（包括COX回归）的方法消除了可能性表达或估计功能的基线危害。然后，需要使用非参数方法分别估算基线危害。这导致了难以解释的累积发生率的逐步估计。使用案例碱采样，Hanley＆Miettinen（2009）解释了如何使用逻辑回归估算参数危害函数。他们的方法自然会导致对累积率平稳率的估计。在本文中，我们介绍了Casebase R软件包，这是一种用于参数生存分析的全面而灵活的工具包。我们描述了如何在更复杂的设置中使用案例框架：竞争风险，时变曝光和可变选择。我们的软件包还包括广泛的可视化工具，以补充事件时间数据的分析。我们通过四个不同的案例研究来说明所有这些功能。 *SRB和MT为这项工作做出了同样的贡献。
In epidemiological studies of time-to-event data, a quantity of interest to the clinician and the patient is the risk of an event given a covariate profile. However, methods relying on time matching or risk-set sampling (including Cox regression) eliminate the baseline hazard from the likelihood expression or the estimating function. The baseline hazard then needs to be estimated separately using a non-parametric approach. This leads to step-wise estimates of the cumulative incidence that are difficult to interpret. Using case-base sampling, Hanley & Miettinen (2009) explained how the parametric hazard functions can be estimated using logistic regression. Their approach naturally leads to estimates of the cumulative incidence that are smooth-in-time. In this paper, we present the casebase R package, a comprehensive and flexible toolkit for parametric survival analysis. We describe how the case-base framework can also be used in more complex settings: competing risks, time-varying exposure, and variable selection. Our package also includes an extensive array of visualization tools to complement the analysis of time-to-event data. We illustrate all these features through four different case studies. *SRB and MT contributed equally to this work.
