Statistical Consulting Unit, StaBLab, Department of Statistics, Ludwig-Maximilians-Universität Mänchen, Ludwigstr. 33, Munich, Germany.
Department of Statistics, Ludwig-Maximilians-Universität Mänchen, Ludwigstr. 33, Munich, Germany.
Biostatistics. 2019 Apr 1;20(2):315-331. doi: 10.1093/biostatistics/kxy003.
We propose a novel approach for the flexible modeling of complex exposure-lag-response associations in time-to-event data, where multiple past exposures within a defined time window are cumulatively associated with the hazard. Our method allows for the estimation of a wide variety of effects, including potentially smooth and smoothly time-varying effects as well as cumulative effects with leads and lags, taking advantage of the inference methods that have recently been developed for generalized additive mixed models. We apply our method to data from a large observational study of intensive care patients in order to analyze the association of both the timing and the amount of artificial nutrition with the short term survival of critically ill patients. We evaluate the properties of the proposed method by performing extensive simulation studies and provide a systematic comparison with related approaches.
我们提出了一种新的方法，用于灵活地建模时间事件数据中复杂的暴露-滞后-反应关联，其中在定义的时间窗口内的多个过去暴露与危险相关联。我们的方法允许估计各种各样的效果，包括潜在的平滑和随时间变化的效果以及具有领先和滞后的累积效果，利用最近为广义加性混合模型开发的推理方法。我们将我们的方法应用于来自大型重症监护患者观察性研究的数据，以分析人工营养的时机和数量与危重病患者短期生存的关联。我们通过进行广泛的模拟研究来评估所提出方法的性质，并与相关方法进行系统比较。
