[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125812-en":3,"doc-seo-125812-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125812,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Simulation-based design optimization for statistical power - Utilizing machine learning","Planning adequately powered research designs increasingly requires more than selecting a suitable sample size. Complex situations call for simultaneous tuning of multiple design parameters and often rely on Monte Carlo simulation when analytic power formulas are unavailable. The optimization target can also incorporate monetary or other costs, yielding either minimum-cost designs at a desired power level or maximum-power designs under a cost threshold. A surrogate modeling framework based on machine learning predictions is proposed, and its efficiency is demonstrated across diverse hypothesis-testing scenarios.","Zurich Open Repository and Archive  \nUniversity of Zurich  \nUniversity Library Strickhofstrasse 39  \nCH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch)  \nYear: 2023  \nSimulation-based design optimization for statistical power: Utilizing machine  \nlearning  \nZimmer, Felix ; Debelak, Rudolf  \nDOI: [https://doi.org/10.1037/met0000611](https://doi.org/10.1037/met0000611)  \nPosted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-253242](https://doi.org/10.5167/uzh-253242)  \nJournal Article Accepted Version  \nOriginally published at:  \nZimmer, Felix; Debelak, Rudolf (2023) . Simulation-based design optimization for statistical power: Utilizing machine learning. Psychological Methods:Epub ahead of print.  \nDOI: [https://doi.org/10.1037/met0000611](https://doi.org/10.1037/met0000611)  \nSimulation-based Design Optimization for Statistical Power:  \nUtilizing Machine Learning  \nFelix Zimmer and Rudolf Debelak  \nUniversity of Zurich  \nAugust 14, 2023  \n© 2023, American Psychological Association. This paper is not the copy of record and may not exactly replicate the 􀀜nal, authoritative version of the article. Please do not copy or cite without authors’ permission. The 􀀜nal article will be available, upon publication, via its DOI: 10.1037/met0000611  \nAuthor Note  \nFelix Zimmer  [https://orcid.org/0000-0002-8127-0007](https://orcid.org/0000-0002-8127-0007)  \nRudolf Debelak  [https://orcid.org/0000-0001-8900-2106](https://orcid.org/0000-0001-8900-2106)  \nThis material is based upon work supported by the Swiss National Science Foundation under Grant No. 188929 awarded to Rudolf Debelak.  \nSome of the ideas and results in this work were disseminated as part of conference talks at the Congress of the German Psychological Society in September 2022, the International Meeting of the Psychometric Society in July 2022, and the Conference of the Deutsche Arbeitsgemeinschaft Statistik in March 2022 . Some ideas are also described in a tutorial paper to our software implementation which is available at [https://doi.org/10.31234/osf.io/r9w6t](https://doi.org/10.31234/osf.io/r9w6t).  \nWe thank Yannick Rothacher for his help in proof reading a draft of the submitted manuscript.  \nThe accompanying R package and analysis code for this study are available at the Open Science Framework and can be accessed at [https://osf.io/szn26/](https://osf.io/szn26/) .  \nCorrespondence concerning this article should be addressed to Felix Zimmer, Psychological Methods, Evaluation and Statistics, Department of Psychology, University of Zurich, Binzmuehlestrasse 14, Box 27, 8050 Zurich, Switzerland. E-mail: fe[lix.zimmer@uzh.ch](lix.zimmer@uzh.ch)  \nDESIGN OPTIMIZATION UTILIZING MACHINE LEARNING 2  \nAbstract  \nThe planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand simultaneous tuning of multiple design parameter dimensions and can only be addressed using Monte Carlo simulation if no analytical approach is available. In addition, cost considerations, e.g., in terms of monetary costs, are a relevant target for optimization. In this context, optimal design parameters can imply a desired level of power at minimum cost or maximum power at a cost threshold. We introduce a surrogate modeling framework based on machine learning predictions to solve these optimization tasks. In a simulation study, we demonstrate the efficiency for a wide range of hypothesis testing scenarios with single- and multidimensional design parameters, including t-tests, ANOVA, item response theory models, multilevel models, and multiple imputation. Our framework provides an algorithmic solution for optimizing study designs when no analytic power analysis is available, handling multiple design dimensions and cost considerations. Our implementation is publicly available in the R package mlpwr.  \nTranslational Abstract  \nThe planning of adequately powered research desi","cbCail3hvQZNalM7","https://ap.wps.com/l/cbCail3hvQZNalM7","pdf",1187262,1,41,"English","en",105,"# Abstract\n# Translational Abstract\n# Keywords\n# Author Information","[{\"question\":\"What problem does the framework address in research planning?\",\"answer\":\"It addresses the need to plan study designs with adequate statistical power when more than sample size must be tuned simultaneously and analytic power analysis is not available.\"},{\"question\":\"How does the method reduce reliance on Monte Carlo simulation?\",\"answer\":\"It uses surrogate modeling with machine learning to approximate the relationship between design parameters and statistical power, then evaluates promising parameters during iterative optimization.\"},{\"question\":\"What optimization goals can be handled?\",\"answer\":\"The framework can target desired power at minimum cost or maximize power within a specified cost threshold, incorporating cost considerations into the design search.\"}]","Simulation-based design optimization for statistical power - Utilizing machine learning | PDF",1785901346,103,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"simulation-based-design-optimization-for-statistical-power-utilizing-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/simulation-based-design-optimization-for-statistical-power-utilizing-machine-learning/125812/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the framework address in research planning?","Question",{"text":75,"@type":76},"It addresses the need to plan study designs with adequate statistical power when more than sample size must be tuned simultaneously and analytic power analysis is not available.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method reduce reliance on Monte Carlo simulation?",{"text":80,"@type":76},"It uses surrogate modeling with machine learning to approximate the relationship between design parameters and statistical power, then evaluates promising parameters during iterative optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"What optimization goals can be handled?",{"text":84,"@type":76},"The framework can target desired power at minimum cost or maximize power within a specified cost threshold, incorporating cost considerations into the design search.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]