{"id":"https://openalex.org/W7162473497","doi":"https://doi.org/10.48550/arxiv.2605.25934","title":"Weighted NPMLE for the Marginal Mean of Recurrent Events with a Competing Terminal Event","display_name":"Weighted NPMLE for the Marginal Mean of Recurrent Events with a Competing Terminal Event","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162473497","doi":"https://doi.org/10.48550/arxiv.2605.25934"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2605.25934","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.25934","pdf_url":"https://arxiv.org/pdf/2605.25934","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2605.25934","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089346732","display_name":"Anna Bellach","orcid":"https://orcid.org/0000-0002-3476-0097","observed_orcids":["https://orcid.org/0000-0002-3476-0097"]},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bellach, Anna","raw_affiliation_strings":["Department of Biostatistics , University of North Carolina at Chapel Hill , NC 27599 , U.S.A","Department of Statistics and Operations Research , University of North Carolina at Chapel Hill , NC 27599 , U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics , University of North Carolina at Chapel Hill , NC 27599 , U.S.A","institution_ids":["https://openalex.org/I114027177"]},{"raw_affiliation_string":"Department of Statistics and Operations Research , University of North Carolina at Chapel Hill , NC 27599 , U.S.A","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132581601","display_name":"Michael Kosorok","orcid":"https://orcid.org/0000-0002-6070-9738","observed_orcids":["https://orcid.org/0000-0002-6070-9738"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kosorok, Michael R.","raw_affiliation_strings":["Department of Biostatistics and"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics and","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.8560000061988831,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.8560000061988831,"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/T13500","display_name":"Probability and Statistical Research","score":0.0640999972820282,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.023900000378489494,"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/recurrent-events","display_name":"recurrent events","score":0.8420000076293945},{"id":"https://openalex.org/keywords/semiparametric-regression","display_name":"semiparametric regression","score":0.9399999976158142},{"id":"https://openalex.org/keywords/right-censoring","display_name":"right censoring","score":0.7289999723434448},{"id":"https://openalex.org/keywords/time-dependent-covariates","display_name":"time-varying covariates","score":0.7929999828338623},{"id":"https://openalex.org/keywords/sandwich-estimator","display_name":"sandwich estimator","score":0.843999981880188},{"id":"https://openalex.org/keywords/asymptotic-normality","display_name":"asymptotic normality","score":0.8149999976158142},{"id":"https://openalex.org/keywords/acute-exacerbation-of-copd","display_name":"COPD exacerbations","score":0.8050000071525574},{"id":"https://openalex.org/keywords/simvastatin","display_name":"simvastatin","score":0.9900000095367432},{"id":"https://openalex.org/keywords/personalized-prediction","display_name":"personalized prediction","score":0.7400000095367432}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.733299970626831},{"id":"https://openalex.org/C197656967","wikidata":"https://www.wikidata.org/wiki/Q17058458","display_name":"Marginal model","level":3,"score":0.71670001745224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6686000227928162},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6672000288963318},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5760999917984009},{"id":"https://openalex.org/C19539793","wikidata":"https://www.wikidata.org/wiki/Q7449609","display_name":"Semiparametric regression","level":3,"score":0.550000011920929},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.501800000667572},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.44519999623298645},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.42559999227523804},{"id":"https://openalex.org/C2779664074","wikidata":"https://www.wikidata.org/wiki/Q3518405","display_name":"Terminal (telecommunication)","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C78297888","wikidata":"https://www.wikidata.org/wiki/Q7449607","display_name":"Semiparametric model","level":3,"score":0.40790000557899475},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.40619999170303345},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.40139999985694885},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C26831200","wikidata":"https://www.wikidata.org/wiki/Q16963953","display_name":"Marginal structural model","level":3,"score":0.35580000281333923},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C33114746","wikidata":"https://www.wikidata.org/wiki/Q4672282","display_name":"Accelerated failure time model","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C136336373","wikidata":"https://www.wikidata.org/wiki/Q5611303","display_name":"Grouped data","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C106195933","wikidata":"https://www.wikidata.org/wiki/Q7847935","display_name":"Truncation (statistics)","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.2671000063419342},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C204016326","wikidata":"https://www.wikidata.org/wiki/Q5401384","display_name":"Estimating equations","level":3,"score":0.2563999891281128}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2605.25934","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.25934","pdf_url":"https://arxiv.org/pdf/2605.25934","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2605.25934","doi":"https://doi.org/10.48550/arxiv.2605.25934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25934","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2605.25934","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.25934","pdf_url":"https://arxiv.org/pdf/2605.25934","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8048999905586243,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7162473497.pdf","grobid_xml":"https://content.openalex.org/works/W7162473497.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2227906117","https://openalex.org/W2302053089","https://openalex.org/W54702858","https://openalex.org/W7097893681","https://openalex.org/W4414861847","https://openalex.org/W2768369303","https://openalex.org/W4390340178","https://openalex.org/W2316375461","https://openalex.org/W4394855920","https://openalex.org/W2974975188"],"abstract_inverted_index":{"Regression":[0],"modeling":[1],"of":[2,62,73,95,103,119,131,141,164,176,182],"recurrent":[3,64,121],"and":[4,77,92,98,178],"terminal":[5,191],"events":[6],"continues":[7],"to":[8,149],"present":[9],"methodological":[10],"challenges":[11],"in":[12],"survival":[13],"analysis.":[14],"Existing":[15],"approaches":[16],"either":[17],"make":[18],"unverifiable":[19],"assumptions":[20],"about":[21],"the":[22,26,33,38,59,63,84,90,96,104,115,120,132,142,152,162,174,180],"dependency":[23],"structure":[24],"between":[25],"two":[27],"event":[28,192],"types":[29],"or":[30],"rely":[31],"on":[32,50,83],"proportional":[34],"intensity":[35,118],"assumption":[36],"for":[37,166,173,186,193],"marginal":[39,60,85,116],"mean.":[40],"A":[41],"semiparametric":[42,74],"regression":[43,75],"model":[44,68],"is":[45,48,145],"proposed":[46,143],"that":[47,112,160],"based":[49],"a":[51,70,100,108,128,155,189],"novel":[52,109],"weighted":[53,133],"likelihood":[54],"function,":[55],"thereby":[56],"targeting":[57],"directly":[58,113],"mean":[61,86,117],"event.":[65],"Our":[66],"general":[67],"captures":[69],"large":[71,156],"class":[72],"models":[76],"accommodates":[78],"external":[79],"time-dependent":[80],"covariate":[81],"effects":[82],"intensity.":[87],"We":[88,106,169],"establish":[89],"consistency":[91],"asymptotic":[93],"normality":[94],"estimators":[97],"propose":[99,107],"sandwich":[101],"estimator":[102],"variance.":[105],"simulation":[110,124],"procedure":[111],"targets":[114],"events.":[122],"In":[123],"studies,":[125],"we":[126],"demonstrate":[127],"strong":[129],"performance":[130],"NPMLE":[134],"under":[135],"independent":[136],"right-censoring.":[137],"The":[138],"practical":[139],"utility":[140],"methodology":[144],"demonstrated":[146],"through":[147],"application":[148],"data":[150],"from":[151],"STATCOPE":[153],"trial,":[154],"randomized":[157],"clinical":[158],"trial":[159],"investigated":[161],"efficacy":[163],"simvastatin":[165,183],"COPD":[167],"exacerbations.":[168],"provide":[170],"personalized":[171],"predictions":[172],"number":[175],"exacerbations":[177],"reassess":[179],"effect":[181],"treatment,":[184],"accounting":[185],"death":[187],"as":[188],"competing":[190],"patients":[194],"with":[195],"GOLD":[196],"stage":[197],"4.":[198]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-05-27T00:00:00"}
