{"id":"https://openalex.org/W1999164342","doi":"https://doi.org/10.1214/14-ba922","title":"Generalized Quantile Treatment Effect: A Flexible Bayesian Approach Using Quantile Ratio Smoothing","display_name":"Generalized Quantile Treatment Effect: A Flexible Bayesian Approach Using Quantile Ratio Smoothing","publication_year":2015,"publication_date":"2015-02-02","ids":{"openalex":"https://openalex.org/W1999164342","doi":"https://doi.org/10.1214/14-ba922","mag":"1999164342"},"language":"en","primary_location":{"id":"doi:10.1214/14-ba922","doi":"https://doi.org/10.1214/14-ba922","is_oa":true,"landing_page_url":"https://doi.org/10.1214/14-ba922","pdf_url":null,"source":{"id":"https://openalex.org/S127898559","display_name":"Bayesian Analysis","issn_l":"1931-6690","issn":["1931-6690","1936-0975"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","doyens","jufo-2","norway-2","oarl","poland-140","russia-white-list-1"],"host_organization":"https://openalex.org/P4310321640","host_organization_name":"International Society for Bayesian Analysis","host_organization_lineage":["https://openalex.org/P4310321640"],"host_organization_lineage_names":["International Society for Bayesian Analysis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bayesian Analysis","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1214/14-ba922","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sergio Venturini","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sergio Venturini","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007008626","display_name":"Francesca Dominici","orcid":"https://orcid.org/0000-0002-9382-0141","observed_orcids":["https://orcid.org/0000-0002-9382-0141","https://orcid.org/0000-0001-5964-0756"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Francesca Dominici","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5050977500","display_name":"Giovanni Parmigiani","orcid":"https://orcid.org/0000-0002-8783-5961","observed_orcids":["https://orcid.org/0000-0002-8783-5961","https://orcid.org/0000-0002-4791-3072"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Giovanni Parmigiani","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2447,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63337261,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":"10","issue":"3","first_page":"523","last_page":"552"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9940999746322632,"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":0.9940999746322632,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.0038999998942017555,"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":0.00019999999494757503,"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/bayesian-inference","display_name":"Bayesian inference","score":0.5669999718666077},{"id":"https://openalex.org/keywords/medicare-spending","display_name":"Medicare spending","score":0.703000009059906}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.8870999813079834},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.652400016784668},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5687999725341797},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.552299976348877},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5181999802589417},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4406000077724457},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41179999709129333},{"id":"https://openalex.org/C47121976","wikidata":"https://www.wikidata.org/wiki/Q3489473","display_name":"Quantile function","level":4,"score":0.39820000529289246},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.36489999294281006},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3605000078678131},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3057999908924103},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.303600013256073},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29109999537467957},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.28139999508857727},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2653999924659729},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":9,"locations":[{"id":"doi:10.1214/14-ba922","doi":"https://doi.org/10.1214/14-ba922","is_oa":true,"landing_page_url":"https://doi.org/10.1214/14-ba922","pdf_url":null,"source":{"id":"https://openalex.org/S127898559","display_name":"Bayesian Analysis","issn_l":"1931-6690","issn":["1931-6690","1936-0975"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","doyens","jufo-2","norway-2","oarl","poland-140","russia-white-list-1"],"host_organization":"https://openalex.org/P4310321640","host_organization_name":"International Society for Bayesian Analysis","host_organization_lineage":["https://openalex.org/P4310321640"],"host_organization_lineage_names":["International Society for Bayesian Analysis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bayesian Analysis","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1509.01042","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1509.01042","pdf_url":"https://arxiv.org/pdf/1509.01042","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":"pmh:oai:biostats.bepress.com:harvardbiostat-1191","doi":null,"is_oa":true,"landing_page_url":"https://biostats.bepress.com/harvardbiostat/paper181","pdf_url":"https://biostats.bepress.com/harvardbiostat/paper181","source":{"id":"https://openalex.org/S4377196124","display_name":"Collection of Biostatistics Research Archive","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Harvard University Biostatistics Working Paper Series","raw_type":"text"},{"id":"pmh:oai:iris.unito.it:2318/1713857","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/2318/1713857","pdf_url":"http://hdl.handle.net/2318/1713857","source":{"id":"https://openalex.org/S4306400637","display_name":"Institutional Research Information System University of Turin (University of Turin)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I55143463","host_organization_name":"University of Turin","host_organization_lineage":["https://openalex.org/I55143463"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.684.3763","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.684.3763","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://biostats.bepress.com/cgi/viewcontent.cgi?article%3D1191%26context%3Dharvardbiostat","raw_type":"text"},{"id":"pmh:oai:CULeuclid:euclid.ba/1422884982","doi":null,"is_oa":false,"landing_page_url":"http://projecteuclid.org/euclid.ba/1422884982","pdf_url":null,"source":{"id":"https://openalex.org/S4306400787","display_name":"Project Euclid (Cornell University)","issn_l":null,"issn":null,"is_oa":false,"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":"","raw_type":"Text"},{"id":"pmh:oai:iris.unibocconi.it:11565/3984453","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/11565/3984453","pdf_url":null,"source":{"id":"https://openalex.org/S7407055170","display_name":"Archivio istituzionale della Ricerca - Bocconi","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.1509.01042","doi":"https://doi.org/10.48550/arxiv.1509.01042","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1509.01042","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":"article-journal"},{"id":"mag:634764388","doi":null,"is_oa":false,"landing_page_url":"http://biostats.bepress.com/cgi/viewcontent.cgi?article=1191&context=harvardbiostat","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.1214/14-ba922","doi":"https://doi.org/10.1214/14-ba922","is_oa":true,"landing_page_url":"https://doi.org/10.1214/14-ba922","pdf_url":null,"source":{"id":"https://openalex.org/S127898559","display_name":"Bayesian Analysis","issn_l":"1931-6690","issn":["1931-6690","1936-0975"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","doyens","jufo-2","norway-2","oarl","poland-140","russia-white-list-1"],"host_organization":"https://openalex.org/P4310321640","host_organization_name":"International Society for Bayesian Analysis","host_organization_lineage":["https://openalex.org/P4310321640"],"host_organization_lineage_names":["International Society for Bayesian Analysis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bayesian Analysis","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4088889987","display_name":"A TRANSLATIONAL FRAMEWORK FOR METHODOLOGICAL RIGOR TO IMPROVE PATIENT CENTERED OU","funder_award_id":"K18HS021991","funder_id":"https://openalex.org/F4320332177","funder_display_name":"Agency for Healthcare Research and Quality"},{"id":"https://openalex.org/G6095022823","display_name":null,"funder_award_id":"K18 HS021991","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6413571270","display_name":"Statistiscal methods for population health research on chemical mixtures","funder_award_id":"R01ES012054","funder_id":"https://openalex.org/F4320337361","funder_display_name":"National Institute of Environmental Health Sciences"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320332177","display_name":"Agency for Healthcare Research and Quality","ror":"https://ror.org/03jmfdf59"},{"id":"https://openalex.org/F4320337361","display_name":"National Institute of Environmental Health Sciences","ror":"https://ror.org/00j4k1h63"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1999164342.pdf","grobid_xml":"https://content.openalex.org/works/W1999164342.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W253646976","https://openalex.org/W583278954","https://openalex.org/W658722133","https://openalex.org/W1559250156","https://openalex.org/W1566333899","https://openalex.org/W1585566614","https://openalex.org/W1967611219","https://openalex.org/W1968201538","https://openalex.org/W1984524168","https://openalex.org/W1985093013","https://openalex.org/W1993919176","https://openalex.org/W1993921219","https://openalex.org/W2007856058","https://openalex.org/W2027653451","https://openalex.org/W2045656233","https://openalex.org/W2059029763","https://openalex.org/W2077866122","https://openalex.org/W2095516703","https://openalex.org/W2096904991","https://openalex.org/W2106706098","https://openalex.org/W2113039194","https://openalex.org/W2124758339","https://openalex.org/W2130416410","https://openalex.org/W2134901991","https://openalex.org/W2145592821","https://openalex.org/W2151967501","https://openalex.org/W2159490819","https://openalex.org/W2170219541","https://openalex.org/W2577227262","https://openalex.org/W3122568481","https://openalex.org/W3123574146","https://openalex.org/W3123753898","https://openalex.org/W3124338191","https://openalex.org/W4241653265","https://openalex.org/W4242484299","https://openalex.org/W4292691288"],"related_works":["https://openalex.org/W634764388","https://openalex.org/W3174091463","https://openalex.org/W4416596591","https://openalex.org/W3036485625","https://openalex.org/W1967923839","https://openalex.org/W4308242964","https://openalex.org/W4224436850","https://openalex.org/W7097216198","https://openalex.org/W194268815","https://openalex.org/W2890802079"],"abstract_inverted_index":{"We":[0,170],"propose":[1,161],"a":[2,11,15,37,51,61,99,109,147,162,180],"new":[3],"general":[4],"approach":[5,178],"for":[6,59,151,167],"estimating":[7,60],"the":[8,23,34,40,43,54,65,70,77,84,88,103,113,121,134,143,152,155,168,205,214,217],"effect":[9,27,63,68],"of":[10,39,79,83,102,112,146,154,176],"binary":[12],"treatment":[13,26,45,62,67,72],"on":[14],"continuous":[16],"and":[17,69,125,232],"potentially":[18],"highly":[19],"skewed":[20],"response":[21],"variable,":[22],"generalized":[24],"quantile":[25,71,123],"(GQTE).":[28],"The":[29,136],"GQTE":[30],"is":[31,106,142],"defined":[32],"as":[33,108],"difference":[35,215],"between":[36,223],"function":[38,111],"quantiles":[41,105],"under":[42,87],"two":[44,89,104,122],"conditions.":[46],"As":[47,196],"such,":[48],"it":[49,75,189],"represents":[50],"generalization":[52],"over":[53],"standard":[55],"approaches":[56],"typically":[57],"used":[58],"(i.e.,":[64,225],"average":[66],"effect)":[73],"because":[74],"allows":[76,117],"comparison":[78],"any":[80],"arbitrary":[81],"characteristic":[82],"outcome\u2019s":[85],"distribution":[86,132],"treatments.":[90],"Following":[91],"Dominici":[92],"et":[93],"al.":[94],"(2005),":[95],"we":[96,140,160,199],"assume":[97],"that":[98,185],"pre-specified":[100],"transformation":[101],"modeled":[107],"smooth":[110],"percentiles.":[114],"This":[115],"assumption":[116],"us":[118],"to":[119,127,133,204,212],"link":[120],"functions":[124],"thus":[126],"borrow":[128],"information":[129],"from":[130],"one":[131],"other.":[135],"main":[137],"theoretical":[138],"contribution":[139],"provide":[141],"analytical":[144],"derivation":[145],"closed":[148],"form":[149],"expression":[150],"likelihood":[153],"model.":[156],"Exploiting":[157],"this":[158],"result":[159],"novel":[163],"Bayesian":[164],"inferential":[165],"methodology":[166,203],"GQTE.":[169],"show":[171],"some":[172,187],"finite":[173],"sample":[174],"properties":[175],"our":[177,202],"through":[179],"simulation":[181],"study":[182],"which":[183],"confirms":[184],"in":[186,216],"cases":[188,224],"performs":[190],"better":[191],"than":[192],"other":[193],"nonparametric":[194],"methods.":[195],"an":[197],"illustration":[198],"finally":[200],"apply":[201],"1987":[206],"National":[207],"Medicare":[208],"Expenditure":[209],"Survey":[210],"data":[211],"estimate":[213],"single":[218],"hospitalization":[219],"medical":[220],"cost":[221],"distributions":[222],"subjects":[226],"affected":[227],"by":[228],"smoking":[229],"attributable":[230],"diseases)":[231],"controls.":[233]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2016-06-24T00:00:00"}
