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000147704 041__ $$aeng
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000147704 1001_ $$aDe Bin, Riccardo$$b0
000147704 245__ $$aCombining clinical and molecular data in regression prediction models: insights from a simulation study.
000147704 260__ $$aOxford [u.a.]$$bOxford University Press$$c2020
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000147704 500__ $$a2020 Dec 1;21(6):1904-1919
000147704 520__ $$aData integration, i.e. the use of different sources of information for data analysis, is becoming one of the most important topics in modern statistics. Especially in, but not limited to, biomedical applications, a relevant issue is the combination of low-dimensional (e.g. clinical data) and high-dimensional (e.g. molecular data such as gene expressions) data sources in a prediction model. Not only the different characteristics of the data, but also the complex correlation structure within and between the two data sources, pose challenging issues. In this paper, we investigate these issues via simulations, providing some useful insight into strategies to combine low- and high-dimensional data in a regression prediction model. In particular, we focus on the effect of the correlation structure on the results, while accounting for the influence of our specific choices in the design of the simulation study.
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000147704 7001_ $$aBoulesteix, Anne-Laure$$b1
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000147704 7001_ $$0P:(DE-He78)ecb33fb615e08035fdcefcaebfdff8f0$$aBecker, Natalia$$b3
000147704 7001_ $$aSauerbrei, Willi$$b4
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