Statistical Model Building Strategies for Cardiologic Applications (SAMBA)

心脏病学应用统计模型构建策略 (SAMBA)

• 批准号: 437007492
• 负责人: Professor Dr. Heiko Becher
• 金额: --
• 依托单位: Institut für Medizinische Statistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/437007492?language=en
• 关键词: Statistical; Model; Building; Strategies; Cardiologic

Statistical models that adequately describe disease progression and treatment response are essential for development, improvement and judgment of therapies in all medical fields. The aims of such models are- to compare the outcome of interest between different interventions or levels of a modifiable risk factor while adjusting for confounding variables, - to identify explanatory variables and concisely describe the association between explanatory variables and the outcome of interest, and - to make predictions for the outcome of interest. The methodological needs and challenges for these three distinct aims are different. In medical applications, researchers often do not focus on a single aim but the interest lies in a combination. Generally, the development of a valid descriptive or explanatory model relies on the identification of a meaningfully sized set of explanatory variables and the specification of adequate functional forms. Intensive statistical research on both aspects was performed for decades. However, the results of this research are only poorly incorporated into clinical research. This interdisciplinary project intends to build a bridge between statistical research on model building strategies and implementation of the methodology into actual medical research. Therefore, this project aims at1. identifying deficiencies in multivariable models that were developed for cardiologic applications with respect to statistical model building (selection of variables and functional forms), 2. building real advanced statistical models for four typical cardiologic research questions by applying state-of-the-art methodology, 3. developing and evaluating new methods to correct for overestimation bias arising in data-driven model building, and4. providing guidance for model building strategies which are understandable for applied researchersTherefore, from a statistical point of view, the aim is to identify, discuss and improve the current standards applied in clinical research with respect to model building and variable identification. In this project, we focus on descriptive models, potentially combined with the aim to make a best possible prediction under possible sample size limitations. By this, the aim of the project lies in models where regression coefficients must be interpretable from a medical point of view. We will particularly address the impact of sample size and highlight options and limitations as occurring in real life situations. From a medical point of view, the aim is to gain new medical insights from statistical models which are built by employing better methodology. As a comprehensive result, we expect to be able to deduce methodologically improved and valid model building strategies for each of the four exemplary applications. We will use several original data sources of cardiovascular studies and combine them with results from the corresponding medical literature.

充分描述疾病进展和治疗反应的统计模型对于所有医学领域的治疗方法的发展、改进和判断是必不可少的。这些模型的目的是--在调整混杂变量的同时，比较不同干预措施或可改变风险因素水平之间的利益结果，--确定解释变量，简明扼要地描述解释变量与利益结果之间的联系，以及--对利益结果作出预测。这三个不同目标的方法需求和挑战是不同的。在医学应用中，研究人员往往不专注于一个单一的目标，但兴趣在于组合。一般而言，开发有效的描述性或解释性模型依赖于确定一组有意义的解释变量和指定适当的函数形式。几十年来，对这两个方面进行了密集的统计研究。然而，这项研究的结果只被很少地纳入临床研究。这一跨学科项目旨在搭建一座桥梁，在关于模型建立战略的统计研究和将方法论落实到实际医学研究之间。因此，本项目的目标是1。识别为心脏病学应用开发的多变量模型在统计模型建立(变量和函数形式的选择)方面的不足，2.通过应用最先进的方法为四个典型的心脏病学研究问题建立真正的高级统计模型，3.开发和评估新的方法，以纠正数据驱动的模型建立中产生的高估偏差，以及4.为应用研究人员理解的模型建立策略提供指导因此，从统计学的角度来看，目的是确定、讨论和改进目前在临床研究中应用的关于模型建立和变量识别的标准。在这个项目中，我们将重点放在描述性模型上，潜在地结合目标是在可能的样本量限制下做出最好的可能预测。因此，该项目的目标在于从医学角度来看，回归系数必须是可解释的模型。我们将特别讨论样本量的影响，并强调现实生活中发生的各种选择和限制。从医学的角度来看，目的是从统计模型中获得新的医学见解，这些模型是通过采用更好的方法建立的。作为一个全面的结果，我们期望能够为四个示范应用中的每一个推导出方法上改进的和有效的模型构建策略。我们将使用心血管研究的几个原始数据来源，并将它们与相应的医学文献的结果结合起来。