Unit of Clinical Biostatistics, Aalborg University Hospital, Sdr Skovvej 15, Aalborg, 9000, Denmark.
Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
BMC Med Res Methodol. 2021 Feb 15;21(1):36. doi: 10.1186/s12874-021-01227-8.
Time-to-event data that is subject to interval censoring is common in the practice of medical research and versatile statistical methods for estimating associations in such settings have been limited. For right censored data, non-parametric pseudo-observations have been proposed as a basis for regression modeling with the possibility to use different association measures. In this article, we propose a method for calculating pseudo-observations for interval censored data.
We develop an extension of a recently developed set of parametric pseudo-observations based on a spline-based flexible parametric estimator. The inherent competing risk issue with an interval censored event of interest necessitates the use of an illness-death model, and we formulate our method within this framework. To evaluate the empirical properties of the proposed method, we perform a simulation study and calculate pseudo-observations based on our method as well as alternative approaches. We also present an analysis of a real dataset on patients with implantable cardioverter-defibrillators who are monitored for the occurrence of a particular type of device failures by routine follow-up examinations. In this dataset, we have information on exact event times as well as the interval censored data, so we can compare analyses of pseudo-observations based on the interval censored data to those obtained using the non-parametric pseudo-observations for right censored data.
Our simulations show that the proposed method for calculating pseudo-observations provides unbiased estimates of the cumulative incidence function as well as associations with exposure variables with appropriate coverage probabilities. The analysis of the real dataset also suggests that our method provides estimates which are in agreement with estimates obtained from the right censored data.
The proposed method for calculating pseudo-observations based on the flexible parametric approach provides a versatile solution to the specific challenges that arise with interval censored data. This solution allows regression modeling using a range of different association measures.
在医学研究实践中，时常会遇到存在区间删失的事件时间数据，而针对此类数据的关联估计，统计方法的选择十分有限。对于右删失数据，可以采用非参数拟似观测值作为回归建模的基础，并有可能使用不同的关联度量。本文提出了一种计算区间删失数据拟似观测值的方法。
我们基于基于样条的灵活参数估计，对最近开发的一组参数拟似观测值进行扩展。对于感兴趣的区间删失事件，存在固有的竞争风险问题，需要使用疾病死亡模型，我们在该框架内构建了我们的方法。为了评估所提出方法的经验性质，我们进行了模拟研究，并基于我们的方法和其他替代方法计算了拟似观测值。我们还呈现了对植入式心脏复律除颤器患者的真实数据集的分析，这些患者通过常规随访检查监测特定类型设备故障的发生。在该数据集中，我们有确切的事件时间以及区间删失数据的信息，因此我们可以将基于区间删失数据的拟似观测值分析与使用右删失数据的非参数拟似观测值分析进行比较。
我们的模拟结果表明，所提出的计算拟似观测值的方法可以提供累积发生率函数的无偏估计以及与暴露变量的关联，且具有适当的覆盖概率。真实数据集的分析也表明，我们的方法提供的估计值与从右删失数据获得的估计值一致。
基于灵活参数方法的拟似观测值计算方法为区间删失数据所带来的特定挑战提供了一种通用的解决方案。该解决方案允许使用多种不同的关联度量进行回归建模。
