{"id":"https://openalex.org/W4400244058","doi":"https://doi.org/10.1080/10618600.2024.2374576","title":"Dynamic Prediction Using Landmark Historical Functional Cox Regression","display_name":"Dynamic Prediction Using Landmark Historical Functional Cox Regression","publication_year":2024,"publication_date":"2024-07-02","ids":{"openalex":"https://openalex.org/W4400244058","doi":"https://doi.org/10.1080/10618600.2024.2374576"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2024.2374576","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2374576","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","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/A5085973114","display_name":"Andrew Leroux","orcid":"https://orcid.org/0000-0002-6355-3580"},"institutions":[{"id":"https://openalex.org/I51713134","display_name":"University of Colorado Anschutz Medical Campus","ror":"https://ror.org/03wmf1y16","country_code":"US","type":"education","lineage":["https://openalex.org/I51713134"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew Leroux","raw_affiliation_strings":["Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus","Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus","institution_ids":["https://openalex.org/I51713134"]},{"raw_affiliation_string":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA","institution_ids":["https://openalex.org/I51713134"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089030792","display_name":"Ciprian M. Crainiceanu","orcid":"https://orcid.org/0000-0001-6601-3881"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ciprian Crainiceanu","raw_affiliation_strings":["Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University","Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]},{"raw_affiliation_string":"Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085973114"],"corresponding_institution_ids":["https://openalex.org/I51713134"],"apc_list":null,"apc_paid":null,"fwci":0.5008,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6513306,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"34","issue":"1","first_page":"59","last_page":"71"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9915000200271606,"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":0.9915000200271606,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9776999950408936,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9639000296592712,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.8051248788833618},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5553139448165894},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5285590887069702},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5152238011360168},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4611620604991913},{"id":"https://openalex.org/keywords/proportional-hazards-model","display_name":"Proportional hazards model","score":0.4290062487125397},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34995782375335693},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3360816240310669},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33136165142059326},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.32987770438194275},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31399017572402954}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.8051248788833618},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5553139448165894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5285590887069702},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5152238011360168},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4611620604991913},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.4290062487125397},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34995782375335693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3360816240310669},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33136165142059326},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32987770438194275},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31399017572402954}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2024.2374576","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2374576","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.41999998688697815,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W313080928","https://openalex.org/W628731042","https://openalex.org/W1423766661","https://openalex.org/W1556637198","https://openalex.org/W1846287826","https://openalex.org/W1969709904","https://openalex.org/W1970654520","https://openalex.org/W1984272022","https://openalex.org/W1990420052","https://openalex.org/W2013365346","https://openalex.org/W2016136817","https://openalex.org/W2027515083","https://openalex.org/W2031381243","https://openalex.org/W2031518017","https://openalex.org/W2034945547","https://openalex.org/W2054192965","https://openalex.org/W2070330801","https://openalex.org/W2109242212","https://openalex.org/W2117653440","https://openalex.org/W2140308441","https://openalex.org/W2145997692","https://openalex.org/W2149514650","https://openalex.org/W2150634422","https://openalex.org/W2410237123","https://openalex.org/W2468874071","https://openalex.org/W2497318341","https://openalex.org/W2582643776","https://openalex.org/W2593563251","https://openalex.org/W2593643174","https://openalex.org/W2735776056","https://openalex.org/W2775351331","https://openalex.org/W2792449492","https://openalex.org/W2793636163","https://openalex.org/W3011751388","https://openalex.org/W3134873896","https://openalex.org/W3147894994","https://openalex.org/W4239330499"],"related_works":["https://openalex.org/W2387623956","https://openalex.org/W2800090224","https://openalex.org/W31220157","https://openalex.org/W3083243621","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W1963858542","https://openalex.org/W2165884543","https://openalex.org/W2316704084"],"abstract_inverted_index":{"Dynamic":[0],"prediction":[1,76],"of":[2,8,14,24,29,77,89,105,125,170],"survival":[3,33,78],"data":[4,25],"in":[5,174],"the":[6,30,53,87,90,146],"presence":[7],"time-varying":[9,47,91],"covariates":[10,48],"is":[11,58,103,149,158],"an":[12,164],"area":[13],"active":[15],"research.":[16],"Two":[17],"common":[18],"analytic":[19],"approaches":[20],"for":[21,55,61,67,74,111,181],"this":[22,182],"type":[23],"are":[26,161,184],"joint":[27,56,113,135,143,147],"modeling":[28,57,144],"longitudinal":[31,83],"and":[32,35,64,131,152],"processes":[34],"landmarking.":[36,68],"However,":[37],"there":[38],"has":[39],"been":[40],"little":[41],"work":[42],"dedicated":[43],"to":[44,129,142,168],"densely":[45,81],"measured":[46,82],"using":[49,80],"either":[50],"approach.":[51],"Moreover,":[52],"software":[54,110],"slow,":[59],"especially":[60],"large":[62,176],"datasets,":[63],"rather":[65],"limited":[66],"We":[69],"propose":[70],"a":[71,98,175],"landmark":[72,95,138],"approach":[73,102,139],"dynamic":[75],"outcomes":[79],"predictors,":[84],"which":[85,126],"treats":[86],"past":[88],"covariate":[92],"at":[93],"each":[94],"point":[96],"as":[97],"functional":[99],"predictor.":[100],"This":[101],"orders":[104],"magnitude":[106],"faster":[107],"than":[108],"existing":[109],"simpler":[112],"models.":[114,136],"Our":[115,137],"extensive":[116],"comparative":[117],"simulation":[118],"study":[119],"required":[120],"8.4":[121],"computation-years,":[122],"over":[123],"99%":[124],"was":[127],"devoted":[128],"fitting":[130],"predicting":[132,166],"from":[133],"two":[134],"performs":[140],"similarly":[141],"when":[145,156],"model":[148],"correctly":[150],"specified":[151],"substantially":[153],"out-performs":[154],"it":[155,157],"not.":[159],"Methods":[160],"motivated":[162],"by":[163],"application":[165],"time":[167],"recovery":[169],"Multiple":[171],"Sclerosis":[172],"lesions":[173],"neuroimaging":[177],"dataset.":[178],"Supplementary":[179],"materials":[180],"article":[183],"available":[185],"online.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
