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000276080 1001_ $$00000-0002-8947-440X$$aRahnenführer, Jörg$$b0
000276080 245__ $$aStatistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.
000276080 260__ $$aHeidelberg [u.a.]$$bSpringer$$c2023
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000276080 520__ $$aIn high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 'High-dimensional data' of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.
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000276080 650_7 $$2Other$$aAnalytical goals
000276080 650_7 $$2Other$$aClustering
000276080 650_7 $$2Other$$aExploratory data analysis
000276080 650_7 $$2Other$$aHigh-dimensional data
000276080 650_7 $$2Other$$aInitial data analysis
000276080 650_7 $$2Other$$aMultiple testing
000276080 650_7 $$2Other$$aOmics data
000276080 650_7 $$2Other$$aPrediction
000276080 650_7 $$2Other$$aSTRATOS initiative
000276080 650_2 $$2MeSH$$aHumans
000276080 650_2 $$2MeSH$$aGoals
000276080 650_2 $$2MeSH$$aBiomedical Research
000276080 650_2 $$2MeSH$$aResearch Design
000276080 7001_ $$00000-0002-7441-6880$$aDe Bin, Riccardo$$b1
000276080 7001_ $$0P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aBenner, Axel$$b2$$udkfz
000276080 7001_ $$00000-0001-9358-011X$$aAmbrogi, Federico$$b3
000276080 7001_ $$00000-0002-8981-2421$$aLusa, Lara$$b4
000276080 7001_ $$00000-0002-2729-0947$$aBoulesteix, Anne-Laure$$b5
000276080 7001_ $$aMigliavacca, Eugenia$$b6
000276080 7001_ $$00000-0002-5666-8662$$aBinder, Harald$$b7
000276080 7001_ $$00000-0002-6963-2968$$aMichiels, Stefan$$b8
000276080 7001_ $$00000-0002-6792-4123$$aSauerbrei, Willi$$b9
000276080 7001_ $$00000-0001-8195-3206$$aMcShane, Lisa$$b10
000276080 7001_ $$adata”, for topic group “High-dimensional$$b11$$eCollaboration Author
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