A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19.
A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19.
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DOI：
10.1177/09622802221106720
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发表时间：
2022-09
影响因子：
2.3
通讯作者：
De Angelis, Daniela
中科院分区：
文献类型：
--
作者：
Jackson, Christopher H.;Tom, Brian D. M.;Kirwan, Peter D.;Mandal, Sema;Seaman, Shaun R.;Kunzmann, Kevin;Presanis, Anne M.;De Angelis, Daniela
我们比较了两个多状态建模框架，这些框架可用于表示流行病期间感染者入院后的事件日期。这些方法被应用于COVID-19住院患者的数据，以估计进入重症监护室的概率，重症监护室入院前后患者在医院死亡的概率，住院时间以及所有这些如何随年龄和性别而变化。一个建模框架的基础是为竞争性风险界定过渡特有的危险函数。一个不太常用的框架定义了将经历每个后续事件的部分潜伏亚群，并使用混合模型来估计个体将经历每个事件的概率，以及事件发生的时间分布。我们以COVID-19为例，比较了这两种框架的优缺点。这些问题包括模型参数的解释，估计感兴趣的数量的计算效率，在软件中实现和评估拟合优度。在这个例子中，我们发现有些群体发生某些事件的风险似乎很低，特别是重症监护病房的入院，这些都是最好的代表使用“治愈率”模型来定义过渡特定的危险。我们提供通用软件来实现我们在flexsurv R软件包中描述的所有模型，该软件包允许使用任意灵活的分布来表示特定原因的危险或事件时间。
We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using ‘cure-rate’ models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.
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影响因子：
2.7
作者：
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通讯作者：
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