{"id":"https://openalex.org/W4399758017","doi":"https://doi.org/10.1093/biomtc/ujae045","title":"Doubly robust estimation and sensitivity analysis for marginal structural quantile models","display_name":"Doubly robust estimation and sensitivity analysis for marginal structural quantile models","publication_year":2024,"publication_date":"2024-03-27","ids":{"openalex":"https://openalex.org/W4399758017","doi":"https://doi.org/10.1093/biomtc/ujae045","pmid":"https://pubmed.ncbi.nlm.nih.gov/38884127"},"language":"en","primary_location":{"id":"doi:10.1093/biomtc/ujae045","doi":"https://doi.org/10.1093/biomtc/ujae045","is_oa":false,"landing_page_url":"https://doi.org/10.1093/biomtc/ujae045","pdf_url":null,"source":{"id":"https://openalex.org/S8265502","display_name":"Biometrics","issn_l":"0006-341X","issn":["0006-341X","1541-0420"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-b","doyens","jufo-2","ki-jl-2","medline","norway-2","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biometrics","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2210.04100","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100420595","display_name":"Chao Cheng","orcid":"https://orcid.org/0000-0003-3234-5874","observed_orcids":["https://orcid.org/0000-0003-3234-5874"]},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chao Cheng","raw_affiliation_strings":["Center for Methods in Implementation and Prevention Science, Yale School of Public Health , New Haven, CT 06510 , United States","Department of Biostatistics, Yale School of Public Health , New Haven, CT 06510 , United States"],"raw_orcid":"https://orcid.org/0000-0003-3234-5874","affiliations":[{"raw_affiliation_string":"Center for Methods in Implementation and Prevention Science, Yale School of Public Health , New Haven, CT 06510 , United States","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Department of Biostatistics, Yale School of Public Health , New Haven, CT 06510 , United States","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054202053","display_name":"Liangyuan Hu","orcid":"https://orcid.org/0000-0002-4067-892X","observed_orcids":["https://orcid.org/0000-0002-4067-892X"]},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liangyuan Hu","raw_affiliation_strings":["Department of Biostatistics and Epidemiology, Rutgers University , New Brunswick, NJ 08854 , United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics and Epidemiology, Rutgers University , New Brunswick, NJ 08854 , United States","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100373539","display_name":"Fan Li","orcid":"https://orcid.org/0000-0001-6183-1893","observed_orcids":["https://orcid.org/0000-0001-6183-1893","https://orcid.org/0000-0002-0390-3673"]},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fan Li","raw_affiliation_strings":["Center for Methods in Implementation and Prevention Science, Yale School of Public Health , New Haven, CT 06510 , United States","Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine , New Haven, CT 06510 , United States","Department of Biostatistics, Yale School of Public Health , New Haven, CT 06510 , United States"],"raw_orcid":"https://orcid.org/0000-0001-6183-1893","affiliations":[{"raw_affiliation_string":"Center for Methods in Implementation and Prevention Science, Yale School of Public Health , New Haven, CT 06510 , United States","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine , New Haven, CT 06510 , United States","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Department of Biostatistics, Yale School of Public Health , New Haven, CT 06510 , United States","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100373539","https://openalex.org/A5100420595"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":{"value":3969,"currency":"USD","value_usd":3969},"apc_paid":null,"fwci":1.0295,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.76183705,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"80","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9990000128746033,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9990000128746033,"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/T10136","display_name":"Statistical Methods and Inference","score":0.0005000000237487257,"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":9.999999747378752e-05,"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/doubly-robust-estimation","display_name":"doubly robust estimation","score":0.9900000095367432},{"id":"https://openalex.org/keywords/sensitivity-analysis","display_name":"sensitivity analysis","score":0.875},{"id":"https://openalex.org/keywords/semiparametric-efficiency","display_name":"semiparametric efficiency","score":0.8939999938011169},{"id":"https://openalex.org/keywords/sequential-ignorability","display_name":"sequential ignorability","score":0.824999988079071},{"id":"https://openalex.org/keywords/time-varying-treatment-effects","display_name":"time-varying treatment effects","score":0.843999981880188},{"id":"https://openalex.org/keywords/potential-outcomes","display_name":"potential outcomes","score":0.5230000019073486},{"id":"https://openalex.org/keywords/antihypertensive-drugs","display_name":"antihypertensive drugs","score":0.8190000057220459},{"id":"https://openalex.org/keywords/severe-hypertension","display_name":"severe hypertension","score":0.8659999966621399},{"id":"https://openalex.org/keywords/electronic-health-records","display_name":"electronic health records","score":0.7229999899864197}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7574151754379272},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.7443190813064575},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5473976731300354},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.5388801097869873},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5133875608444214},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4854350686073303},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.47610434889793396},{"id":"https://openalex.org/C26831200","wikidata":"https://www.wikidata.org/wiki/Q16963953","display_name":"Marginal structural model","level":3,"score":0.4597357511520386},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4242270290851593},{"id":"https://openalex.org/C204016326","wikidata":"https://www.wikidata.org/wiki/Q5401384","display_name":"Estimating equations","level":3,"score":0.41921207308769226},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.41749852895736694},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38550275564193726},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37264811992645264},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35502028465270996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1464025378227234},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000959","descriptor_name":"Antihypertensive Agents","qualifier_ui":"Q000627","qualifier_name":"therapeutic use","is_major_topic":false},{"descriptor_ui":"D001699","descriptor_name":"Biometry","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006973","descriptor_name":"Hypertension","qualifier_ui":"Q000188","qualifier_name":"drug therapy","is_major_topic":true},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1093/biomtc/ujae045","doi":"https://doi.org/10.1093/biomtc/ujae045","is_oa":false,"landing_page_url":"https://doi.org/10.1093/biomtc/ujae045","pdf_url":null,"source":{"id":"https://openalex.org/S8265502","display_name":"Biometrics","issn_l":"0006-341X","issn":["0006-341X","1541-0420"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-b","doyens","jufo-2","ki-jl-2","medline","norway-2","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biometrics","raw_type":"journal-article"},{"id":"pmid:38884127","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38884127","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biometrics","raw_type":"Journal Article"},{"id":"pmh:oai:arXiv.org:2210.04100","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.04100","pdf_url":"https://arxiv.org/pdf/2210.04100","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2210.04100","doi":"https://doi.org/10.48550/arxiv.2210.04100","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2210.04100","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:2210.04100","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.04100","pdf_url":"https://arxiv.org/pdf/2210.04100","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G155589275","display_name":null,"funder_award_id":"ME-2021C2-23685","funder_id":"https://openalex.org/F4320308927","funder_display_name":"Patient-Centered Outcomes Research Institute"}],"funders":[{"id":"https://openalex.org/F4320308380","display_name":"Yale University","ror":"https://ror.org/03v76x132"},{"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/W4399758017.pdf","grobid_xml":"https://content.openalex.org/works/W4399758017.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1622753297","https://openalex.org/W1963509555","https://openalex.org/W2031392253","https://openalex.org/W2051334102","https://openalex.org/W2074005688","https://openalex.org/W2091580968","https://openalex.org/W2099811086","https://openalex.org/W2111947080","https://openalex.org/W2133519275","https://openalex.org/W2137370054","https://openalex.org/W2185431622","https://openalex.org/W3026313505","https://openalex.org/W3123436326","https://openalex.org/W3125092920","https://openalex.org/W3173171400","https://openalex.org/W4213234500","https://openalex.org/W4296824223"],"related_works":["https://openalex.org/W4414846925","https://openalex.org/W2396607988","https://openalex.org/W7172029156","https://openalex.org/W7113091248","https://openalex.org/W4307565023","https://openalex.org/W7221570365","https://openalex.org/W4376543391","https://openalex.org/W4366459398","https://openalex.org/W3130705291","https://openalex.org/W2899449644"],"abstract_inverted_index":{"The":[0],"marginal":[1],"structure":[2],"quantile":[3],"model":[4],"(MSQM)":[5],"provides":[6],"a":[7,16,41,99,117],"unique":[8],"lens":[9],"to":[10,97,103,121,140,155,166,173,185],"understand":[11],"the":[12,20,27,32,37,56,65,72,90,108,123,130,142,147,152,156,168,180,183],"causal":[13],"effect":[14,169],"of":[15,23,64,107,125,146,170,182],"time-varying":[17,189],"treatment":[18,69],"on":[19],"full":[21],"distribution":[22],"potential":[24,73],"outcomes.":[25],"Under":[26],"semiparametric":[28,81],"framework,":[29],"we":[30,95,115],"derive":[31],"efficiency":[33],"influence":[34],"function":[35,119],"for":[36,48],"MSQM,":[38],"from":[39],"which":[40],"new":[42],"doubly":[43,57,91],"robust":[44,58,92],"estimator":[45,59],"is":[46,60,76,80,134],"proposed":[47,148,153],"point":[49,109],"estimation":[50],"and":[51,79,110,178,188],"inference.":[52],"We":[53,150],"show":[54],"that":[55],"consistent":[61],"if":[62,83],"either":[63],"models":[66,85],"associated":[67],"with":[68,175],"assignment":[70],"or":[71],"outcome":[74],"distributions":[75],"correctly":[77],"specified,":[78],"efficient":[82,105],"both":[84],"are":[86,138],"correct.":[87],"To":[88],"implement":[89],"MSQM":[93,127],"estimator,":[94],"propose":[96],"solve":[98],"smoothed":[100],"estimating":[101],"equation":[102],"facilitate":[104],"computation":[106],"variance":[111],"estimates.":[112],"In":[113],"addition,":[114],"develop":[116],"confounding":[118],"approach":[120],"investigate":[122],"sensitivity":[124],"several":[126],"estimators":[128],"when":[129],"sequential":[131],"ignorability":[132],"assumption":[133],"violated.":[135],"Extensive":[136],"simulations":[137],"conducted":[139],"examine":[141],"finite-sample":[143],"performance":[144],"characteristics":[145],"methods.":[149],"apply":[151],"methods":[154],"Yale":[157],"New":[158],"Haven":[159],"Health":[160,163],"System":[161],"Electronic":[162],"Record":[164],"data":[165],"study":[167],"antihypertensive":[171],"medications":[172],"patients":[174],"severe":[176],"hypertension":[177],"assess":[179],"robustness":[181],"findings":[184],"unmeasured":[186],"baseline":[187],"confounding.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2024-06-18T00:00:00"}