Boosting copulas - multivariate distributional regression for digital medicine

传统的回归模型往往对生物医学领域的当代数据问题的复杂关联和关系提供过于简单化的看法。特别是，正确捕获多个临床终点之间的相关关联对于避免模型错误规范具有很高的相关性，这可能导致有偏差的结果，甚至错误或误导性的结论和治疗。因此，为生物医学中的此类问题量身定制统计方法的方法学发展具有相当大的意义。该项目的目标是通过将高效的高维数据统计学习工具和多元数据结构经济学的既定方法结合起来，为高维生物医学数据结构开发新的条件联结回归模型，这些方法允许捕获变量之间复杂的依赖结构。这些方法将使我们能够同时对多个端点的整个联合分布进行建模，并通过最初在统计和机器学习领域提出的算法自动确定相关的影响协变量和风险因素。由此产生的模型既可以用于解释和分析复杂的关联结构，也可以用于预测推理（多个端点的同时预测间隔）。开放软件的其他实现及其在各种研究中的应用突出了该项目在数字医学领域的方法发展的潜力。

Traditional regression models often provide an overly simplistic view on complex associations and relationships to contemporary data problems in the area of biomedicine. In particular, capturing relevant associations between multiple clinical endpoints correctly is of high relevance to avoid model misspecifications, which can lead tobiased results and even wrong or misleading conclusions and treatments. As such, methodological development of statistical methods tailored for such problems in biomedicine are of considerable interest. It is the aim of this project to develop novel conditional copula regression models for high-dimensional biomedical data structures by bringing together efficient statistical learning tools for high-dimensional data and established methods from economics for multivariate data structures that allow to capture complex dependence structuresbetween variables. These methods will allow us to model the entire joint distribution of multiple endpoints simultaneously and to automatically determine the relevant influential covariates and risk factors via algorithms originally proposed in the area of statistical and machine learning. The resulting models can thenbe used both for the interpretation and analysis of complex association-structures as well as for prediction inference (simultaneous prediction intervals for multiple endpoints). Additional implementation in open software and its application in various studies highlight the potentials of this project’s methodological developments in the area of digital medicine.