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GAMLSS for biostatistical regression modeling. Refinements and Further Developments

用于生物统计回归建模的 GAMLSS。

基本信息

批准号：
217090301
负责人：
Professor Dr. Matthias Schmid
金额：
--
依托单位：
Institut für Medizinische Biometrie, Informatik und Epidemiologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2012
资助国家：
德国
起止时间：
2011-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/217090301?language=en
关键词：
GAMLSS biostatistical regression modeling.Refinements

项目摘要

In view of the rapid development of personalized therapies and diagnostic tools, high-dimensional data analysis has gained considerable importance in biomedical research. A challenge for biostatistical method development is therefore to extract relevant information from various (possibly high-dimensional) data sources and to combine this information to give accurate statistical prediction rules. To this purpose, the first period of DFG Project was concerned with the development of AUC-optimized prediction rules for binary and censored clinical outcome variables.Because of the more and more detailed collection of clinical and anamnestic information in biomedical studies, and also because of the rapid development of technologies for generating molecular data, an increasingly important task for biostatisticians is to develop new methods that incorporate prior biological or clinical knowledge into statistical prediction rules. Consequently, there is a need for combining the data-driven methods developed in the first project period with the optimization of regression models that can be adapted to existing knowledge and information.A highly promising tool to address these issues is the GAMLSS methodology, which allows for the flexible modeling of a large variety of continuous and categorical outcome variables. For this reason GAMLSS methods are the main subject of the work program of the proposed second project period. In particular, they allow for the specification of flexible mean and variance structures according to prior knowledge. In addition, variable selection in GAMLSS is possible via the techniques developed in the first project period.Despite the many advantages of GAMLSS, there are several limitations that currently tend to prevent the broad use of GAMLSS in biomedical research. These limitations concern the construction of valid hypothesis tests in high-dimensional settings but also the non-consideration of uncertainty in the estimation of prognosis intervals via GAMLSS. Moreover, there are no extensions to multi-dimensional outcome variables. The main goal of the proposed project is therefore to analyze the aforementioned limitations of GAMLSS and to develop methods to solve the resulting problems.The evaluation of the newly developed methods will, in particular, comprise the analysis of clinical and epidemiological study data. This analysis will be based on current research and collaboration projects of the applicants. In addition, newly developed methods will be implemented and made available to users via open source software.

鉴于个性化治疗和诊断工具的快速发展，高维数据分析在生物医学研究中具有相当重要的意义。因此，生物统计方法开发的挑战是从各种（可能是高维的）数据源中提取相关信息，并将这些信息联合收割机组合起来，以给出准确的统计预测规则。为此，DFG项目的第一阶段关注的是开发二进制和删失临床结局变量的AUC优化预测规则。由于生物医学研究中临床和既往信息的收集越来越详细，并且由于分子数据生成技术的快速发展，生物统计学家的一项日益重要的任务是开发新的方法，将先前的生物学或临床知识纳入统计预测规则。因此，需要将第一个项目期间开发的数据驱动方法与能够适应现有知识和信息的回归模型的优化相结合。解决这些问题的一个非常有前途的工具是GAMLSS方法，它允许对各种连续和分类结果变量进行灵活建模。因此，GAMLSS方法是拟议的第二个项目期间工作计划的主要主题。特别是，他们允许灵活的均值和方差结构的规格，根据先验知识。此外，变量的选择在GAMLSS是可能的，通过在第一个项目期间开发的技术。尽管有许多优点的GAMLSS，有几个限制，目前往往会阻止广泛使用的GAMLSS在生物医学研究。这些局限性涉及在高维环境中构建有效的假设检验，但也不考虑通过GAMLSS估计预后区间的不确定性。此外，没有多维结果变量的扩展。因此，拟议项目的主要目标是分析GAMLSS的上述局限性，并开发解决由此产生的问题的方法，特别是对新开发方法的评估将包括对临床和流行病学研究数据的分析。这一分析将基于申请人目前的研究和合作项目。此外，将采用新开发的方法，并通过开放源码软件提供给用户。