Almohaimeed Amani, Adam Ishag
Department of Statistics and Operations Research, College of Science, Qassim University, Buraydah, Saudi Arabia.
Department of Obstetrics and Gynecology, Unaizah College of Medicine, Qassim University, Unaizah, Saudi Arabia.
BioData Min. 2024 Nov 1;17(1):47. doi: 10.1186/s13040-024-00403-y.
Studies looking into patient and institutional variables linked to extended hospital stays have arisen as a result of the increased focus on severe maternal morbidity and mortality. Understanding the length of hospitalization of patients after delivery is important to gain insights into when hospitals will reach capacity and to predict corresponding staffing or equipment requirements. In Sudan, the distribution of the length of stay during delivery hospitalizations is heavily skewed, with the average length of stay of 2 to 3 days. This study aimed to investigate the use of non-parametric random effect model with Gamma distributed response for analyzing skewed hospital length of stay data in Sudan in neonatal and maternal unit.
We applied Gamma regression models with unknown random effects, estimated using the non-parametric maximum likelihood (NPML) technique [5]. The NPML reduces the heterogeneity in the distribution of the response and produce a robust estimation since it does not require any assumptions on the distribution. The same applies to the log-Gamma link that does not require any transformation for the data distribution and it can handle the outliers in the data points. In this study, the models are fitted with and without covariates and compared using AIC and BIC values.
The findings imply that in the context of health care database investigations, Gamma regression models with non-parametric random effect consistently reduce heterogeneity and improve model accuracy. The generalized linear model with covariates and random effect (k = 4) had the best fit, indicating that Sudanese hospital length of stay data could be classified into four groups with varying average stays influenced by maternal, neonatal, and obstetrics data.
Identifying factors contributing to longer stays allows hospitals to implement strategies for improvement. Non-parametric random effect model with Gamma distributed response effectively accounts for unobserved heterogeneity and individual-level variability, leading to more accurate inferences and improved patient care. Including random effects can significantly affect variable significance in statistical models, emphasizing the need to consider unobserved heterogeneity when analyzing data containing potential individual-level variability. The findings emphasise the importance of making robust methodological choices in healthcare research in order to inform accurate policy decisions.
由于对严重孕产妇发病和死亡的关注度不断提高，对与延长住院时间相关的患者和机构变量的研究应运而生。了解产后患者的住院时间对于洞察医院何时达到容量以及预测相应的人员配备或设备需求非常重要。在苏丹，分娩住院期间的住院时间分布严重偏态，平均住院时间为2至3天。本研究旨在探讨使用具有伽马分布响应的非参数随机效应模型来分析苏丹新生儿和产妇病房中偏态的住院时间数据。
我们应用了具有未知随机效应的伽马回归模型，使用非参数最大似然（NPML）技术进行估计[5]。NPML减少了响应分布中的异质性并产生稳健的估计，因为它不需要对分布做任何假设。对数伽马链接也是如此，它不需要对数据分布进行任何变换，并且可以处理数据点中的异常值。在本研究中，对有协变量和无协变量的模型进行拟合，并使用AIC和BIC值进行比较。
研究结果表明，在医疗保健数据库调查的背景下，具有非参数随机效应的伽马回归模型能够持续减少异质性并提高模型准确性。具有协变量和随机效应（k = 4）的广义线性模型拟合效果最佳，这表明苏丹的住院时间数据可分为四组，其平均住院时间受产妇、新生儿和产科数据的影响各不相同。
识别导致住院时间延长的因素可以使医院实施改进策略。具有伽马分布响应的非参数随机效应模型有效地考虑了未观察到的异质性和个体水平的变异性，从而得出更准确的推断并改善患者护理。纳入随机效应会显著影响统计模型中变量的显著性，强调在分析包含潜在个体水平变异性的数据时需要考虑未观察到的异质性。研究结果强调了在医疗保健研究中做出稳健方法选择以提供准确政策决策依据的重要性。
