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Increasing clinical usefulness of gene signature prediction rules through simplification and validation

通过简化和验证提高基因特征预测规则的临床实用性

基本信息

批准号：
208375936
负责人：
Professorin Dr. Anne-Laure Boulesteix
金额：
--
依托单位：
Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2011
资助国家：
德国
起止时间：
2010-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/208375936?language=en
关键词：
Increasing clinical usefulness gene signature

项目摘要

Hundreds of gene signatures based on high-dimensional data (HDD) have been proposed in the biomedical literature and hundreds of methodological papers are published on issues around deriving, validating and applying prediction methods for such data sets. Nevertheless it is realized that the clinical value of information from HDD is limited and that important principles widely recognized by biometricians in low-dimensional settings tend to be often ignored in the HDD context. However, even in low-dimensional data (LDD) several issues require additional research and some investigations will concentrate in the LD world and consider adoption to HDD. The main emphasis of this project is on key statistical issues related to the development and validation of multivariable regression models. Findings from the methodological part will be used to consider possible improvements attributed to the combined value of clinical and genetic data. For some gene signatures proposed as useful prediction rules in patients with breast cancer we aim to evaluate critically the evidence for their clinical utility. The latter requires systematic reviews including assessment of quality of reporting and suitability of the methods used for meta-analysis. In work package 1 (WP1) we study the problem of identifying influential observations and their impact on selecting a multivariable model. First, approaches proposed in the low-dimensional context will be compared in several examples. In a second step we will consider their usefulness for HDD. Adaption of key parameters will be required. WP2 focuses on methodological issues related to resampling techniques for model selection in multivariate regression, which have been demonstrated to be useful for deriving interpretable prediction models. In particular, methods are developed to extend these techniques to the HD context with a particular emphasis on model averaging procedures and to support decisions in the case of apparently equally important (possibly correlated) predictors. WP3, a direct extension of a work package from the first funding period, is devoted to the construction and evaluation of prediction models making use of both clinical and omics information. WP4 aims to assess the clinical utility of some gene signatures by critically considering available evidence published in the literature. Relevant methodological issues are the quality of reporting, and the suitability of methods used for validation and meta-analysis.

生物医学文献中提出了数百种基于高维数据（HDD）的基因特征，并且发表了数百篇方法论论文，讨论了围绕此类数据集的推导、验证和应用预测方法的问题。然而，人们认识到，从HDD的信息的临床价值是有限的，并且在低维设置中被生物统计学家广泛认可的重要原则往往在HDD上下文中被忽略。然而，即使在低维数据（LDD）中，也有几个问题需要额外的研究，一些调查将集中在LD世界，并考虑采用HDD。该项目的主要重点是与多变量回归模型的开发和验证有关的关键统计问题。方法学部分的结果将用于考虑归因于临床和遗传数据综合价值的可能改进。对于一些基因签名提出有用的预测规则，在乳腺癌患者中，我们的目标是严格评估其临床效用的证据。后者需要进行系统评价，包括评估报告质量和荟萃分析所用方法的适用性。在工作包1（WP 1）中，我们研究了识别有影响的观测及其对选择多变量模型的影响的问题。首先，在低维背景下提出的方法将在几个例子中进行比较。在第二步中，我们将考虑它们对HDD的有用性。将需要调整关键参数。WP2侧重于与多元回归模型选择中的恢复技术相关的方法学问题，这些技术已被证明对导出可解释的预测模型是有用的。特别是，方法的开发，以扩展这些技术的HD上下文，特别强调模型平均程序，并支持决策的情况下，显然同样重要（可能相关）的预测。WP3是第一个资助期工作包的直接延伸，致力于利用临床和组学信息构建和评估预测模型。WP4旨在通过批判性地考虑文献中发表的现有证据来评估一些基因签名的临床效用。相关的方法学问题是报告的质量以及用于验证和荟萃分析的方法的适用性。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning

通过稳健的参数调整改进跨数据源的结果预测

DOI：
10.1007/s00357-020-09368-z
发表时间：
2021
期刊：
Journal of Classification
影响因子：
2
作者：
N. Ellenbach;A.-L. Boulesteix;B. Bischl;K. Unger;R. Hornung
通讯作者：
R. Hornung

Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study

用于分子预后建模的单中心与多中心数据集：模拟研究

DOI：
10.1186/s13014-020-01543-1
发表时间：
2020
期刊：
Radiation Oncology (London, England)
影响因子：
0
作者：
D. Samaga;R. Hornung;H. Braselmann;J. Hess;H. Zitzelsberger;C. Belka;A.-L. Boulesteix;K. Unger
通讯作者：
K. Unger

REMARK guidelines for tumour biomarker study reporting: a remarkable history