000290234 001__ 290234
000290234 005__ 20241111104011.0
000290234 0247_ $$2doi$$a10.1371/journal.pone.0299989
000290234 0247_ $$2pmid$$apmid:38748677
000290234 037__ $$aDKFZ-2024-01050
000290234 041__ $$aEnglish
000290234 082__ $$a610
000290234 1001_ $$00009-0002-0711-6789$$aStolte, Marieke$$b0
000290234 245__ $$aSimulation study to evaluate when Plasmode simulation is superior to parametric simulation in estimating the mean squared error of the least squares estimator in linear regression.
000290234 260__ $$aSan Francisco, California, US$$bPLOS$$c2024
000290234 3367_ $$2DRIVER$$aarticle
000290234 3367_ $$2DataCite$$aOutput Types/Journal article
000290234 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1715954504_22133
000290234 3367_ $$2BibTeX$$aARTICLE
000290234 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000290234 3367_ $$00$$2EndNote$$aJournal Article
000290234 520__ $$aSimulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for both researchers developing new methods and practitioners confronted with the choice of the most suitable method. The term simulation usually refers to parametric simulation, that is, computer experiments using artificial data made up of pseudo-random numbers. Plasmode simulation, that is, computer experiments using the combination of resampling feature data from a real-life dataset and generating the target variable with a known user-selected outcome-generating model, is an alternative that is often claimed to produce more realistic data. We compare parametric and Plasmode simulation for the example of estimating the mean squared error (MSE) of the least squares estimator (LSE) in linear regression. If the true underlying data-generating process (DGP) and the outcome-generating model (OGM) were known, parametric simulation would obviously be the best choice in terms of estimating the MSE well. However, in reality, both are usually unknown, so researchers have to make assumptions: in Plasmode simulation studies for the OGM, in parametric simulation for both DGP and OGM. Most likely, these assumptions do not exactly reflect the truth. Here, we aim to find out how assumptions deviating from the true DGP and the true OGM affect the performance of parametric and Plasmode simulations in the context of MSE estimation for the LSE and in which situations which simulation type is preferable. Our results suggest that the preferable simulation method depends on many factors, including the number of features, and on how and to what extent the assumptions of a parametric simulation differ from the true DGP. Also, the resampling strategy used for Plasmode influences the results. In particular, subsampling with a small sampling proportion can be recommended.
000290234 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
000290234 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000290234 650_2 $$2MeSH$$aComputer Simulation
000290234 650_2 $$2MeSH$$aLeast-Squares Analysis
000290234 650_2 $$2MeSH$$aLinear Models
000290234 650_2 $$2MeSH$$aHumans
000290234 7001_ $$0P:(DE-He78)0d054b6843ace36d1c965b6cb938d1c9$$aSchreck, Nicholas$$b1$$udkfz
000290234 7001_ $$aSlynko, Alla$$b2
000290234 7001_ $$0P:(DE-He78)609d3f1c1420bf59b2332eeab889cb74$$aSaadati, Maral$$b3$$udkfz
000290234 7001_ $$0P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aBenner, Axel$$b4$$udkfz
000290234 7001_ $$aRahnenführer, Jörg$$b5
000290234 7001_ $$aBommert, Andrea$$b6
000290234 773__ $$0PERI:(DE-600)2267670-3$$a10.1371/journal.pone.0299989$$gVol. 19, no. 5, p. e0299989 -$$n5$$pe0299989 -$$tPLOS ONE$$v19$$x1932-6203$$y2024
000290234 8564_ $$uhttps://inrepo02.dkfz.de/record/290234/files/journal.pone.0299989.pdf
000290234 8564_ $$uhttps://inrepo02.dkfz.de/record/290234/files/journal.pone.0299989.pdf?subformat=pdfa$$xpdfa
000290234 909CO $$ooai:inrepo02.dkfz.de:290234$$pVDB
000290234 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)0d054b6843ace36d1c965b6cb938d1c9$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000290234 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)609d3f1c1420bf59b2332eeab889cb74$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ
000290234 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ
000290234 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0
000290234 9141_ $$y2024
000290234 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2022-04-12T10:14:32Z
000290234 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2022-04-12T10:14:32Z
000290234 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2022-04-12T10:14:32Z
000290234 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2022-04-12T10:14:32Z
000290234 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)1040$$2StatID$$aDBCoverage$$bZoological Record$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2023-10-25
000290234 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2023-10-25
000290234 9201_ $$0I:(DE-He78)C060-20160331$$kC060$$lC060 Biostatistik$$x0
000290234 980__ $$ajournal
000290234 980__ $$aVDB
000290234 980__ $$aI:(DE-He78)C060-20160331
000290234 980__ $$aUNRESTRICTED