{"id":"https://openalex.org/W2888520208","doi":"https://doi.org/10.1145/3233547.3233591","title":"Applying Stochastic Process Model to Imputation of Censored Longitudinal Data","display_name":"Applying Stochastic Process Model to Imputation of Censored Longitudinal Data","publication_year":2018,"publication_date":"2018-08-15","ids":{"openalex":"https://openalex.org/W2888520208","doi":"https://doi.org/10.1145/3233547.3233591","mag":"2888520208"},"language":"en","primary_location":{"id":"doi:10.1145/3233547.3233591","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3233547.3233591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"},"type":"conference-paper","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/A5059151412","display_name":"Ilya Y. Zhbannikov","orcid":"https://orcid.org/0000-0002-6502-6514"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ilya Zhbannikov","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051786764","display_name":"Konstantin G. Arbeev","orcid":"https://orcid.org/0000-0002-4195-7832"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Konstantin Arbeev","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085542503","display_name":"Anatoliy I. Yashin","orcid":"https://orcid.org/0000-0003-4262-8894"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anatoliy Yashin","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16070039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"457","last_page":"464"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14509","display_name":"demographic modeling and climate adaptation","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9916999936103821,"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/imputation","display_name":"Imputation (statistics)","score":0.8099005222320557},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7314815521240234},{"id":"https://openalex.org/keywords/framingham-heart-study","display_name":"Framingham Heart Study","score":0.6772537231445312},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.579561710357666},{"id":"https://openalex.org/keywords/longitudinal-data","display_name":"Longitudinal data","score":0.5243942141532898},{"id":"https://openalex.org/keywords/health-and-retirement-study","display_name":"Health and Retirement Study","score":0.48421454429626465},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.44995126128196716},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3787756860256195},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3650102913379669},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2030138373374939},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17829474806785583},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1338813304901123},{"id":"https://openalex.org/keywords/gerontology","display_name":"Gerontology","score":0.1248578429222107},{"id":"https://openalex.org/keywords/framingham-risk-score","display_name":"Framingham Risk Score","score":0.09640830755233765}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8099005222320557},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7314815521240234},{"id":"https://openalex.org/C111815664","wikidata":"https://www.wikidata.org/wiki/Q1440693","display_name":"Framingham Heart Study","level":4,"score":0.6772537231445312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.579561710357666},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.5243942141532898},{"id":"https://openalex.org/C2780176501","wikidata":"https://www.wikidata.org/wiki/Q5690755","display_name":"Health and Retirement Study","level":2,"score":0.48421454429626465},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.44995126128196716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3787756860256195},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3650102913379669},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2030138373374939},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17829474806785583},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1338813304901123},{"id":"https://openalex.org/C74909509","wikidata":"https://www.wikidata.org/wiki/Q10387","display_name":"Gerontology","level":1,"score":0.1248578429222107},{"id":"https://openalex.org/C11783203","wikidata":"https://www.wikidata.org/wiki/Q5478027","display_name":"Framingham Risk Score","level":3,"score":0.09640830755233765},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3233547.3233591","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3233547.3233591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.41999998688697815,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W313080928","https://openalex.org/W612972800","https://openalex.org/W1822348759","https://openalex.org/W1846287826","https://openalex.org/W1930624869","https://openalex.org/W1990667054","https://openalex.org/W1999212197","https://openalex.org/W2030322782","https://openalex.org/W2032138124","https://openalex.org/W2035459227","https://openalex.org/W2037418922","https://openalex.org/W2048092130","https://openalex.org/W2049633694","https://openalex.org/W2062778025","https://openalex.org/W2064186732","https://openalex.org/W2086332688","https://openalex.org/W2107772748","https://openalex.org/W2111700774","https://openalex.org/W2115098571","https://openalex.org/W2118502261","https://openalex.org/W2129228599","https://openalex.org/W2132949918","https://openalex.org/W2153743722","https://openalex.org/W2171074980","https://openalex.org/W2590844674","https://openalex.org/W2963208580","https://openalex.org/W4295184201","https://openalex.org/W4298826872","https://openalex.org/W4302383795"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Longitudinal":[0],"data":[1,16,26,106],"are":[2],"widely":[3],"used":[4],"in":[5,14,59,75],"medicine,":[6],"demography,":[7],"sociology":[8],"and":[9,54,65,102],"other":[10],"areas.":[11],"Incomplete":[12],"observations":[13,74],"such":[15],"often":[17],"confound":[18],"the":[19,83,98],"results":[20],"of":[21,25,50,62,85,87],"analysis.":[22],"A":[23],"plethora":[24],"imputation":[27,86],"methods":[28],"have":[29],"already":[30],"been":[31],"proposed":[32],"to":[33,82,97,110],"alleviate":[34],"this":[35,122],"problem.":[36],"The":[37],"Stochastic":[38],"Process":[39],"Model":[40],"(SPM)":[41],"represents":[42],"a":[43],"general":[44],"framework":[45],"for":[46,71,121],"modeling":[47],"joint":[48],"evolution":[49],"repeatedly":[51],"measured":[52],"variables":[53],"time-to-event":[55],"outcome":[56],"typically":[57],"observed":[58],"longitudinal":[60,77,90],"studies":[61],"aging,":[63],"health":[64],"longevity.":[66],"It":[67],"is":[68],"perfectly":[69],"suitable":[70],"imputing":[72],"missing":[73,89],"censored":[76,88],"data.":[78,91],"We":[79,113],"applied":[80,95],"SPM":[81],"problem":[84],"This":[92],"model":[93],"was":[94],"both":[96],"Framingham":[99],"Heart":[100],"Study":[101,105],"Cardiovascular":[103],"Health":[104],"as":[107,109],"well":[108],"simulated":[111],"datasets.":[112],"also":[114],"present":[115],"an":[116],"R":[117],"package":[118],"stpm":[119],"designed":[120],"purpose.":[123]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}