{"id":"https://openalex.org/W4401375277","doi":"https://doi.org/10.1214/23-aoas1864","title":"Joint mixed membership modeling of multivariate longitudinal and survival data for learning the individualized disease progression","display_name":"Joint mixed membership modeling of multivariate longitudinal and survival data for learning the individualized disease progression","publication_year":2024,"publication_date":"2024-08-06","ids":{"openalex":"https://openalex.org/W4401375277","doi":"https://doi.org/10.1214/23-aoas1864"},"language":"en","primary_location":{"id":"doi:10.1214/23-aoas1864","doi":"https://doi.org/10.1214/23-aoas1864","is_oa":false,"landing_page_url":"https://doi.org/10.1214/23-aoas1864","pdf_url":null,"source":{"id":"https://openalex.org/S112778392","display_name":"The Annals of Applied Statistics","issn_l":"1932-6157","issn":["1932-6157","1941-7330"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-c","doyens","jufo-3","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Annals of Applied Statistics","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076600982","display_name":"Yuyang He","orcid":"https://orcid.org/0000-0001-5817-5805","observed_orcids":["https://orcid.org/0000-0001-5817-5805"]},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yuyang He","raw_affiliation_strings":["Department of Statistics, The Chinese University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100565377","display_name":"Song Xin-yuan","orcid":"https://orcid.org/0009-0005-4209-5671","observed_orcids":["https://orcid.org/0009-0005-4209-5671","https://orcid.org/0000-0002-7760-6399","https://orcid.org/0000-0003-3414-3117"]},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xinyuan Song","raw_affiliation_strings":["Department of Statistics, The Chinese University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101838713","display_name":"Kai Kang","orcid":"https://orcid.org/0000-0002-6707-4616","observed_orcids":["https://orcid.org/0000-0002-6707-4616"]},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Kang","raw_affiliation_strings":["Department of Statistics, Sun Yat-sen University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4324,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60626743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"18","issue":"3","first_page":"1924","last_page":"1946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.4846000075340271,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.4846000075340271,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.3747999966144562,"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/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.10130000114440918,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/alzheimer-s-disease","display_name":"Alzheimer's disease","score":0.9079999923706055},{"id":"https://openalex.org/keywords/mixed-membership-models","display_name":"mixed membership models","score":0.8970000147819519},{"id":"https://openalex.org/keywords/joint-longitudinal-survival-models","display_name":"joint longitudinal-survival models","score":0.7160000205039978},{"id":"https://openalex.org/keywords/biomarkers","display_name":"biomarkers","score":0.5630000233650208},{"id":"https://openalex.org/keywords/cox-proportional-hazards-model","display_name":"Cox proportional hazards model","score":0.9120000004768372},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6019999980926514},{"id":"https://openalex.org/keywords/precision-medicine","display_name":"precision medicine","score":0.6660000085830688},{"id":"https://openalex.org/keywords/adni","display_name":"ADNI","score":0.6029999852180481}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7969938516616821},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.5481371283531189},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.5254101753234863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4958105981349945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4741120934486389},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.44023919105529785},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39480942487716675},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3910790979862213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24777424335479736},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2309248447418213},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09482672810554504},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1214/23-aoas1864","doi":"https://doi.org/10.1214/23-aoas1864","is_oa":false,"landing_page_url":"https://doi.org/10.1214/23-aoas1864","pdf_url":null,"source":{"id":"https://openalex.org/S112778392","display_name":"The Annals of Applied Statistics","issn_l":"1932-6157","issn":["1932-6157","1941-7330"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-c","doyens","jufo-3","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Annals of Applied Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8560000061988831,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1548841436","https://openalex.org/W1557577378","https://openalex.org/W1562946553","https://openalex.org/W1979695470","https://openalex.org/W1979920342","https://openalex.org/W1980394700","https://openalex.org/W1981985976","https://openalex.org/W1985671636","https://openalex.org/W1990643403","https://openalex.org/W1997703636","https://openalex.org/W2006519787","https://openalex.org/W2027106381","https://openalex.org/W2027445478","https://openalex.org/W2028436804","https://openalex.org/W2032758468","https://openalex.org/W2036362108","https://openalex.org/W2056760934","https://openalex.org/W2070235280","https://openalex.org/W2090342357","https://openalex.org/W2105850604","https://openalex.org/W2107408488","https://openalex.org/W2109242212","https://openalex.org/W2110346818","https://openalex.org/W2119047368","https://openalex.org/W2124581301","https://openalex.org/W2138309709","https://openalex.org/W2144154786","https://openalex.org/W2146855196","https://openalex.org/W2153305362","https://openalex.org/W2163287234","https://openalex.org/W2167840686","https://openalex.org/W2312331308","https://openalex.org/W2490872466","https://openalex.org/W2510361771","https://openalex.org/W2520806078","https://openalex.org/W2730242206","https://openalex.org/W2777552396","https://openalex.org/W2787894218","https://openalex.org/W2889885761","https://openalex.org/W2964350306","https://openalex.org/W3006027310","https://openalex.org/W3110997222","https://openalex.org/W3197587210","https://openalex.org/W3202519246","https://openalex.org/W4237180235","https://openalex.org/W4285727445","https://openalex.org/W4293738259","https://openalex.org/W4296471672","https://openalex.org/W4376618018","https://openalex.org/W4386512001","https://openalex.org/W4400377881"],"related_works":["https://openalex.org/W3208030723","https://openalex.org/W4401592357","https://openalex.org/W4386512001","https://openalex.org/W4412203797","https://openalex.org/W7172347543","https://openalex.org/W4393252297","https://openalex.org/W7202128621","https://openalex.org/W2471830986","https://openalex.org/W3137131062","https://openalex.org/W4386442235"],"abstract_inverted_index":{"Patients":[0],"with":[1,157],"Alzheimer\u2019s":[2,183],"disease":[3,10,29,95],"(AD)":[4],"often":[5,106],"exhibit":[6],"substantial":[7],"heterogeneity":[8],"in":[9,108,192],"progression":[11,30,96],"due":[12],"to":[13,83,119,137,165,181],"multiple":[14,127],"genetic":[15],"causes":[16],"for":[17,33,50],"such":[18],"a":[19,44,69,115,153,189],"complex":[20,121],"disease.":[21],"Investigating":[22],"diverse":[23],"subtypes":[24],"of":[25,57,101,126,141,148],"neurodegeneration":[26],"and":[27,36,55,97,122,178],"individualized":[28,94],"is":[31,134,172],"essential":[32],"early":[34],"diagnosis":[35,194],"precision":[37],"medicine.":[38],"In":[39],"this":[40],"article":[41],"we":[42],"present":[43],"novel":[45],"joint":[46,197],"mixed":[47,110],"membership":[48,78],"model":[49,75,118,133],"multivariate":[51],"longitudinal":[52,128],"AD-related":[53],"biomarkers":[54],"time":[56],"AD":[58,193],"diagnosis.":[59],"Unlike":[60],"conventional":[61],"finite":[62],"mixture":[63],"models":[64],"that":[65],"assign":[66],"each":[67],"subject":[68],"single":[70],"subgroup":[71],"membership,":[72],"the":[73,99,139,146,182],"proposed":[74,170],"assigns":[76],"partial":[77],"across":[79],"subgroups,":[80],"allowing":[81],"subjects":[82],"lie":[84],"between":[85],"two":[86],"or":[87],"more":[88],"subgroups.":[89],"This":[90],"flexible":[91],"structure":[92],"enables":[93],"facilitates":[98],"identification":[100],"clinically":[102],"meaningful":[103],"neurological":[104],"statuses":[105],"elusive":[107],"current":[109],"effects":[111,140],"models.":[112,198],"We":[113,151],"employ":[114],"spline-based":[116],"trajectory":[117],"characterize":[120],"possibly":[123],"nonlinear":[124],"patterns":[125],"clinical":[129],"markers.":[130],"A":[131],"Cox":[132],"then":[135],"used":[136],"examine":[138],"time-variant":[142],"risk":[143],"factors":[144],"on":[145],"hazard":[147],"developing":[149],"AD.":[150],"develop":[152],"Bayesian":[154],"method":[155],"coupled":[156],"efficient":[158],"Markov":[159],"chain":[160],"Monte":[161],"Carlo":[162],"sampling":[163],"schemes":[164],"perform":[166],"statistical":[167],"inference.":[168],"The":[169],"approach":[171],"assessed":[173],"through":[174],"extensive":[175],"simulation":[176],"studies":[177],"an":[179],"application":[180],"Disease":[184],"Neuroimaging":[185],"Initiative":[186],"study,":[187],"showing":[188],"better":[190],"performance":[191],"than":[195],"existing":[196]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2024-08-07T00:00:00"}