[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120927-en":3,"doc-seo-120927-105":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},120927,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","A review on statistical and machine learning competing risks methods","A review consolidating competing risks survival methods across statistical and machine-learning communities, using unified notation and consistent interpretation to enable practical comparison. It surveys classical and state-of-the-art extensions that support flexible assumptions, improved prediction, high-dimensional inputs, and missing values. The review also emphasizes available software and provides reproducible R vignettes where possible. Two key challenges are addressed for benchmarking competing-risks approaches: the selection of performance metrics and the reproducibility of reported results.","Edinburgh Research Explorer  \nA review on statistical and machine learning competing risks methods  \nCitation for published version:  \nMonterrubio-Gomez, K, Constantine-Cooke, N & Vallejos, CA 2024, 'A review on statistical and machine learning competing risks methods', Biometrical Journal. [https://doi.org/10.1002/bimj.202300060](https://doi.org/10.1002/bimj.202300060)  \nDigital Object Identifier (DOI):  \n10.1002/bimj.202300060  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPeer reviewed version  \nPublished In:  \nBiometrical Journal  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 11. May. 2024  \nA review on statistical and machine learning competing risks methods  \nKarla Monterrubio-Gmez∗1, Nathan Constantine-Cooke 1,2 , and Catalina A. Vallejos∗∗ 1,3  \n1 MRC Human Genetics Unit, University of Edinburgh, Edinburgh, United Kingdom  \n2 Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK  \n3 The Alan Turing Institute, London, United Kingdom  \nWhen modelling competing risks survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of competing risks survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metricsand reproducibility.  \nKey words: Competing risks; Survival analysis; Time-to-event data; Risk prediction.  \nSupporting Information for this article is available from the author or on the WWW under [http://dx.doi.org/10.1022/bimj.XXXXXXX](http://dx.doi.org/10.1022/bimj.XXXXXXX)  \n1 Introduction  \nSurvival analysis comprises a collection of methods to model the time until an event of interest occurs. Usually, the goal is to estimate the risk of observing the event by a given time or to quantify the relationship between event risk and known covariates. Survival methods are widely used in several fields; including medicine, social sciences, engineering and economics. Survival methods have been reviewed by Cox and Oakes (1984), Carpenter (1997), Klein and Moeschberger (2006) and, more recently, Wang et al. (2019) .  \nA typical element of survival data is censoring, where event times are unknown. This can occur for several reasons, e.g. lost of follow-up. Survival methods such as the popular Cox proportional hazards (CPH) model (Cox, 1972) often assume independent censoring: those who were censored at a specific time are representative of all those who remained at risk.  \nIn some cases, a subject can experience more than one type of mutually exclusive events—typically referred to as competing risks (CR). For instance, a patient can die from different causes (e.g. cancer or noncancer death) . If the main focus is ","cbCaikHceFwUN7B6","https://ap.wps.com/l/cbCaikHceFwUN7B6","pdf",1165121,1,31,"English","en",105,"# Introduction\n## Survival analysis and censoring\n## Competing risks and implications for modeling\n## Motivation and purpose of the review","[{\"question\":\"What problem does the review focus on?\",\"answer\":\"It focuses on modeling and predicting time-to-event outcomes under competing risks, where multiple mutually exclusive event types may occur.\"},{\"question\":\"How does the review help compare methods?\",\"answer\":\"It unifies notation and interpretation across competing-risks approaches, making strengths and limitations easier to compare.\"},{\"question\":\"What two concerns does the review highlight for benchmark studies?\",\"answer\":\"It highlights the choice of performance metrics and the reproducibility of results in competing-risks evaluations.\"}]","A review on statistical and machine learning competing risks methods | PDF",1785732743,78,{"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},"a-review-on-statistical-and-machine-learning-competing-risks-methods","",{"@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/a-review-on-statistical-and-machine-learning-competing-risks-methods/120927/",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","2026-08-03",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 review focus on?","Question",{"text":75,"@type":76},"It focuses on modeling and predicting time-to-event outcomes under competing risks, where multiple mutually exclusive event types may occur.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the review help compare methods?",{"text":80,"@type":76},"It unifies notation and interpretation across competing-risks approaches, making strengths and limitations easier to compare.",{"name":82,"@type":73,"acceptedAnswer":83},"What two concerns does the review highlight for benchmark studies?",{"text":84,"@type":76},"It highlights the choice of performance metrics and the reproducibility of results in competing-risks evaluations.","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"]