Li Yang, Liu Hao, Wang Xiaoshen, Tu Wanzhu
Department of Biostatistics and Health Data Science Indiana University Indianapolis Indiana USA.
Department of Biostatistics and Epidemiology Rutgers School of Public Health Piscataway New Jersey USA.
J R Stat Soc Ser C Appl Stat. 2022 Nov;71(5):1623-1647. doi: 10.1111/rssc.12593. Epub 2022 Sep 15.
Length of stay (LOS) is an essential metric for the quality of hospital care. Published works on LOS analysis have primarily focused on skewed LOS distributions and the influences of patient diagnostic characteristics. Few authors have considered the events that terminate a hospital stay: Both successful discharge and death could end a hospital stay but with completely different implications. Modelling the time to the first occurrence of discharge or death obscures the true nature of LOS. In this research, we propose a structure that simultaneously models the probabilities of discharge and death. The model has a flexible formulation that accounts for both additive and multiplicative effects of factors influencing the occurrence of death and discharge. We present asymptotic properties of the parameter estimates so that valid inference can be performed for the parametric as well as nonparametric model components. Simulation studies confirmed the good finite-sample performance of the proposed method. As the research is motivated by practical issues encountered in LOS analysis, we analysed data from two real clinical studies to showcase the general applicability of the proposed model.
住院时长（LOS）是衡量医院护理质量的一项重要指标。已发表的关于住院时长分析的研究主要集中在偏态的住院时长分布以及患者诊断特征的影响上。很少有作者考虑过终止住院的事件：成功出院和死亡都可能结束住院，但二者的意义截然不同。对首次出院或死亡时间进行建模会掩盖住院时长的真实本质。在本研究中，我们提出了一种同时对出院概率和死亡概率进行建模的结构。该模型具有灵活的公式，能够考虑影响死亡和出院发生的因素的加性和乘性效应。我们给出了参数估计的渐近性质，以便能够对参数模型和非参数模型组件进行有效的推断。模拟研究证实了所提方法具有良好的有限样本性能。由于本研究是受住院时长分析中遇到的实际问题所推动，我们分析了两项真实临床研究的数据，以展示所提模型的普遍适用性。
