{"id":"https://openalex.org/W7154993851","doi":"https://doi.org/10.21105/joss.09458","title":"BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes","display_name":"BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7154993851","doi":"https://doi.org/10.21105/joss.09458"},"language":null,"primary_location":{"id":"doi:10.21105/joss.09458","is_oa":true,"landing_page_url":"https://doi.org/10.21105/joss.09458","pdf_url":"https://joss.theoj.org/papers/10.21105/joss.09458.pdf","source":{"id":"https://openalex.org/S4210214273","display_name":"The Journal of Open Source Software","issn_l":"2475-9066","issn":["2475-9066"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315853","host_organization_name":"Open Journals","host_organization_lineage":["https://openalex.org/P4310315853"],"host_organization_lineage_names":["Open Journals"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Open Source Software","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://joss.theoj.org/papers/10.21105/joss.09458.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130392010","display_name":"Yuqin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuqin Wang","raw_affiliation_strings":["Department of Biostatistics, Brown University, Providence, RI, United States"],"raw_orcid":"https://orcid.org/0009-0003-8345-9318","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Brown University, Providence, RI, United States","institution_ids":["https://openalex.org/I27804330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130390160","display_name":"Keming Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Keming Zhang","raw_affiliation_strings":["Department of Biostatistics, Brown University, Providence, RI, United States"],"raw_orcid":"https://orcid.org/0009-0001-5495-0058","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Brown University, Providence, RI, United States","institution_ids":["https://openalex.org/I27804330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020602402","display_name":"Arman Oganisian","orcid":"https://orcid.org/0000-0002-0437-4611"},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arman Oganisian","raw_affiliation_strings":["Department of Biostatistics, Brown University, Providence, RI, United States"],"raw_orcid":"https://orcid.org/0000-0002-0437-4611","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Brown University, Providence, RI, United States","institution_ids":["https://openalex.org/I27804330"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27804330"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45048976,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"120","first_page":"9458","last_page":"9458"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.33180001378059387,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.33180001378059387,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.2498999983072281,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.07000000029802322,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.6359999775886536},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.6187000274658203},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5981000065803528},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5559999942779541},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.535099983215332},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4672999978065491},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.39340001344680786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6614000201225281},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6359999775886536},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.6187000274658203},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5981000065803528},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5559999942779541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5557000041007996},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.535099983215332},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4672999978065491},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44449999928474426},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3864000141620636},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.3637999892234802},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.26089999079704285},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2583000063896179},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21105/joss.09458","is_oa":true,"landing_page_url":"https://doi.org/10.21105/joss.09458","pdf_url":"https://joss.theoj.org/papers/10.21105/joss.09458.pdf","source":{"id":"https://openalex.org/S4210214273","display_name":"The Journal of Open Source Software","issn_l":"2475-9066","issn":["2475-9066"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315853","host_organization_name":"Open Journals","host_organization_lineage":["https://openalex.org/P4310315853"],"host_organization_lineage_names":["Open Journals"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Open Source Software","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.21105/joss.09458","is_oa":true,"landing_page_url":"https://doi.org/10.21105/joss.09458","pdf_url":"https://joss.theoj.org/papers/10.21105/joss.09458.pdf","source":{"id":"https://openalex.org/S4210214273","display_name":"The Journal of Open Source Software","issn_l":"2475-9066","issn":["2475-9066"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315853","host_organization_name":"Open Journals","host_organization_lineage":["https://openalex.org/P4310315853"],"host_organization_lineage_names":["Open Journals"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Open Source Software","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6823598168","display_name":null,"funder_award_id":"ME-2023C1-31348","funder_id":"https://openalex.org/F4320308927","funder_display_name":"Patient-Centered Outcomes Research Institute"}],"funders":[{"id":"https://openalex.org/F4320308927","display_name":"Patient-Centered Outcomes Research Institute","ror":"https://ror.org/014q65q44"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7154993851.pdf","grobid_xml":"https://content.openalex.org/works/W7154993851.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1919480245","https://openalex.org/W2098545663","https://openalex.org/W2150272490","https://openalex.org/W2153263135","https://openalex.org/W2577537660","https://openalex.org/W3019567365","https://openalex.org/W4318269845","https://openalex.org/W4376253742","https://openalex.org/W4405366913"],"related_works":[],"abstract_inverted_index":{"Observational":[0],"studies":[1],"are":[2,54,77,112,123],"often":[3],"conducted":[4],"to":[5,83,115,125],"estimate":[6],"the":[7,14,69,74,95,138,167,177,191,241],"causal":[8,121,154,194,230],"effect":[9],"of":[10,17,97,132,153,156,169],"medical":[11],"treatments":[12],"on":[13,161],"average":[15],"rate":[16],"a":[18,23,28,59,162,203],"recurrent":[19,139,163,213,219],"event":[20,61,70,87,140,164,236],"outcome":[21,165],"within":[22,190],"specific":[24],"follow-up":[25,239],"window":[26],"in":[27,166],"defined":[29],"target":[30],"population.Recurrent":[31],"events":[32,53],"(e.g.,":[33,62],"hospitalizations,":[34],"relapses,":[35],"and":[36,73,89,136,171,201,206,211,218,232,249,256],"infections)":[37],"may":[38,93],"occur":[39],"multiple":[40],"times":[41],"during":[42],"follow-up.Causal":[43],"estimation":[44,152],"is":[45],"challenging":[46],"with":[47,58],"such":[48],"outcomes":[49,193],"because:":[50],"1)":[51],"Recurrent":[52],"typically":[55],"jointly":[56],"observed":[57,128],"terminal":[60,75,217],"death),":[63],"which":[64,185],"precludes":[65],"future":[66],"recurrences.2)":[67],"Both":[68],"count":[71],"process":[72,76],"unobserved":[78],"after":[79],"possible":[80,107],"dropout":[81],"-leading":[82],"right":[84],"censored":[85],"total":[86],"counts":[88],"survival":[90],"time.3)":[91],"Patients":[92],"initiate":[94],"treatment":[96,116,134,158],"interest":[98],"at":[99],"different":[100,157],"times,":[101],"yielding":[102],"as":[103,106],"many":[104],"strategies":[105,117,160],"initiation":[108,159,199,243],"times.Finally,":[109],"4)":[110],"patients":[111],"not":[113],"assigned":[114],"randomly,":[118],"so":[119,174],"formal":[120],"methods":[122,189],"required":[124],"adjust":[126],"for":[127,150,208],"confounders":[129,205],"-i.e.,":[130],"drivers":[131],"both":[133],"patterns":[135],"either":[137],"or":[141],"death":[142,170],"processes.This":[143],"paper":[144],"presents":[145],"BayCauRETM,":[146],"an":[147,198],"R":[148,223],"package":[149,245],"Bayesian":[151,187],"effects":[155],"presence":[168],"censoring.It":[172],"does":[173],"by":[175,180],"implementing":[176],"methodology":[178],"developed":[179],"Oganisian":[181],"et":[182],"al.":[183],"(2024)":[184],"uses":[186],"statistical":[188],"potential":[192],"inference":[195],"framework.Users":[196],"specify":[197],"time":[200],"supply":[202],"data.framecontaining":[204],"columns":[207],"censoring,":[209],"death,":[210],"interval-specific":[212],"counts,":[214],"then":[215],"define":[216],"models":[220],"via":[221],"standard":[222],"formula":[224],"syntax.Given":[225],"these":[226],"inputs,":[227],"BayCauRETM":[228],"performs":[229],"adjustment":[231],"outputs":[233],"adjusted":[234],"expected":[235],"rates":[237],"over":[238],"under":[240],"specified":[242],"time.The":[244],"also":[246],"provides":[247],"diagnostic":[248],"visualization":[250],"utilities.Intended":[251],"users":[252],"include":[253],"statisticians,":[254],"epidemiologists,":[255],"health-services":[257],"researchers":[258],"analyzing":[259],"observational":[260],"data.":[261]},"counts_by_year":[],"updated_date":"2026-04-22T06:01:30.510260","created_date":"2026-04-21T00:00:00"}
