Modeling Length of Stay in Hospital and Other Right Skewed Data: Comparison of Phase-Type, Gamma and Log-Normal Distributions
Modeling Length of Stay in Hospital and Other Right Skewed Data: Comparison of Phase-Type, Gamma and Log-Normal Distributions
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DOI：
10.1111/j.1524-4733.2008.00421.x
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发表时间：
2009-03-01
期刊：
影响因子：
4.5
通讯作者：
Pettitt, Anthony
中科院分区：
文献类型：
--
作者：
Faddy, Malcolm;Graves, Nicholas;Pettitt, Anthony
提出一种相对新颖的方法来建模住院时间数据，并评估协变量的作用，其中一些与不良事件有关。与基于伽马和对数正态分布的备选模型进行关键比较。为了证明模型拟合不良对决策的影响。该模型将住院过程组织为马尔可夫阶段/状态，描述出院前住院到吸收状态。入院是通过状态1，从第一个状态出院对应的是短期停留，过渡到后面的状态对应的是更长的停留。由此产生的相位型概率分布为停留时间数据提供了一个灵活的建模框架，这些数据已知难以适应其他分布。该数据集包括1901名患者的住院时间和一些协变量的值。拟合模型由6个马尔可夫相组成，与数据拟合良好。备选gamma和对数正态模型拟合不佳，给出的系数估计值不同，协变量效应的统计显著性在模型之间存在差异。适合的模型通常比不适合的模型更可取，因为它们将产生更可靠的统计系数估计。较差的系数估计可能会通过低估或夸大某些事件的成本或通过防止该事件而节省的成本来误导决策者。当从拟合不佳的模型中得出的系数估计可能会产生误导时，没有明显的方法来识别先验。
To present a relatively novel method for modeling length-of-stay data and assess the role of covariates, some of which are related to adverse events. To undertake critical comparisons with alternative models based on the gamma and log-normal distributions. To demonstrate the effect of poorly fitting models on decision-making.The model has the process of hospital stay organized into Markov phases/states that describe stay in hospital before discharge to an absorbing state. Admission is via state 1 and discharge from this first state would correspond to a short stay, with transitions to later states corresponding to longer stays. The resulting phase-type probability distributions provide a flexible modeling framework for length-of-stay data which are known to be awkward and difficult to fit to other distributions.The dataset consisted of 1901 patients' lengths of stay and values for a number of covariates. The fitted model comprised six Markov phases, and provided a good fit to the data. Alternative gamma and log-normal models did not fit as well, gave different coefficient estimates, and statistical significance of covariate effects differed between the models.Models that fit should generally be preferred over those that do not, as they will produce more statistically reliable coefficient estimates. Poor coefficient estimates may mislead decision-makers by either understating or overstating the cost of some event or the cost savings from preventing that event. There is no obvious way of identifying a priori when coefficient estimates from poorly fitting models might be misleading.