Population Health and Genomics, University of Dundee, Dundee, UK.
Department of Mathematics and Computing, Indian Institute of Technology-Dhanbad, Dhanbad, India.
BMC Med Res Methodol. 2024 Aug 6;24(1):172. doi: 10.1186/s12874-024-02289-0.
We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifically designed for the joint modelling of longitudinal and survival data in the context of big data analytics. The jmBIG package offers efficient and scalable implementations of joint modelling algorithms, allowing for integrating large-scale healthcare datasets.By utilizing the capabilities of jmBIG, researchers and analysts can effectively handle the challenges associated with big healthcare data, such as high dimensionality and complex relationships between multiple outcomes.With the support of jmBIG, analysts can seamlessly fit Bayesian joint models, generate predictions, and evaluate the performance of the models. The package incorporates cutting-edge methodologies and harnesses the computational capabilities of parallel computing to accelerate the analysis of large-scale healthcare datasets significantly. In summary, jmBIG empowers researchers to gain deeper insights into disease progression and treatment response, fostering evidence-based decision-making and paving the way for personalized healthcare interventions that can positively impact patient outcomes on a larger scale.
我们引入了 R 包 jmBIG，以方便对大型医疗保健数据集进行分析并开发预测模型。这个包提供了一整套工具和功能，专门针对大数据分析环境中的纵向和生存数据的联合建模而设计。jmBIG 包提供了高效和可扩展的联合建模算法实现，允许集成大规模的医疗保健数据集。通过利用 jmBIG 的功能，研究人员和分析师可以有效地处理与大型医疗保健数据相关的挑战，如高维性和多个结果之间的复杂关系。有了 jmBIG 的支持，分析师可以无缝地拟合贝叶斯联合模型、生成预测，并评估模型的性能。该包采用了最先进的方法，并利用并行计算的计算能力，大大加速了大规模医疗保健数据集的分析。总之，jmBIG 使研究人员能够更深入地了解疾病进展和治疗反应，促进基于证据的决策，并为个性化医疗干预铺平道路，从而在更大范围内对患者结果产生积极影响。
