Integrating information from similar data sites when developing clinical prediction models for patients from a target site (A01)

在为目标站点的患者开发临床预测模型时整合来自相似数据站点的信息 (A01)

• 批准号: 524663484
• 金额: --
• 依托单位: Abteilung für Mathematische Stochastik
• 依托单位国家: 德国
• 项目类别: Collaborative Research Centres
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/524663484?language=en
• 关键词: Integrating; information; similar; data; sites

The project will provide methodology based on weighting of similar data from external sites in a prediction model for a target site. We will investigate different strategies for quantifying similarity, including 1) propensity score-based approaches, 2) similarity quantified parametrically as well as nonparametrically via the difference between the conditional empirical distribution of the outcome given the covariates per site, and 3) a deep learning approach. Besides empirical evaluation, we will provide comprehensive mathematical theory on the parameter estimates of corresponding regression models.

该项目将提供一种方法，其基础是在目标地点的预测模型中对来自外部地点的类似数据进行加权。我们将研究量化相似性的不同策略，包括1）基于倾向评分的方法，2）通过每个站点的协变量的结果的条件经验分布之间的差异来参数化和非参数化量化的相似性，以及3）深度学习方法。除了经验评估，我们将提供全面的数学理论，对相应的回归模型的参数估计。