Statistical Model Building Strategies for Cardiologic Applications (SAMBA)

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开发，改进和判断至关重要。这些模型的目的是-比较不同干预措施或可修改的风险因素水平之间的结果，同时调整混杂变量，-识别解释变量并简要描述解释变量与结果之间的关联，以及-对结果进行预测。 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，研究人员往往不关注单一目标，而是关注组合。一般来说，一个有效的描述性或解释性模型的发展依赖于确定一组有意义的解释变量和适当的功能形式的规格。几十年来，对这两个方面进行了深入的统计研究。然而，这项研究的结果只是很差地纳入临床研究。这个跨学科的项目旨在建立模型构建策略的统计研究和实际医学研究方法的实施之间的桥梁。因此，本项目的目标是1.识别针对心脏病学应用开发的多变量模型中关于统计模型构建（变量和函数形式的选择）的缺陷，2.运用最先进的方法学为四个典型的心脏病学研究问题建立真实的高级统计模型，3.开发和评估新的方法，以纠正数据驱动模型构建中出现的高估偏差;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，我们专注于描述性模型，可能与在可能的样本量限制下做出最佳预测的目标相结合。因此，该项目的目的在于回归系数必须从医学角度解释的模型。我们将特别讨论样本量的影响，并强调在真实的生活情况下发生的选择和限制。从医学的角度来看，其目的是从采用更好的方法建立的统计模型中获得新的医学见解。作为一个全面的结果，我们希望能够推导出方法上的改进和有效的模型构建策略的四个示例性应用程序。我们将使用几个心血管研究的原始数据源，并将它们与相应医学文献的结果联合收割机结合起来。

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.