{"id":"https://openalex.org/W67644556","doi":null,"title":"Nonparametric Bayesian clinical trials design for multivariate patient response","display_name":"Nonparametric Bayesian clinical trials design for multivariate patient response","publication_year":1996,"publication_date":"1996-01-01","ids":{"openalex":"https://openalex.org/W67644556","mag":"67644556"},"language":"en","primary_location":{"id":"pmh:oai:digitalcommons.lib.uconn.edu:dissertations-1229","doi":null,"is_oa":false,"landing_page_url":"https://digitalcommons.lib.uconn.edu/dissertations/AAI9705030","pdf_url":null,"source":{"id":"https://openalex.org/S4377196438","display_name":"OpenCommons - UConn (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"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/A5010108804","display_name":"Pantelis K. Vlachos","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pantelis Vlachos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5010108804"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","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"}},"topics":[{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","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"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.0031999999191612005,"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.0017000000225380063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dirichlet-process-mixture-model","display_name":"Dirichlet process mixture model","score":0.6460000276565552},{"id":"https://openalex.org/keywords/multiway-contingency-tables","display_name":"multidimensional contingency tables","score":0.8019999861717224},{"id":"https://openalex.org/keywords/stopping-criterion","display_name":"stopping rules","score":0.6480000019073486},{"id":"https://openalex.org/keywords/prior-specification","display_name":"prior specification","score":0.5289999842643738},{"id":"https://openalex.org/keywords/backward-induction","display_name":"backward induction","score":0.7289999723434448},{"id":"https://openalex.org/keywords/expected-sample-size","display_name":"expected sample size","score":0.9399999976158142},{"id":"https://openalex.org/keywords/interim-analysis","display_name":"interim analysis","score":0.44200000166893005},{"id":"https://openalex.org/keywords/design-simulation","display_name":"design simulation","score":0.3240000009536743}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7008882164955139},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6396175622940063},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6127175092697144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.457412451505661},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.45109957456588745},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.4351944029331207},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.43213993310928345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4181819558143616},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4046202600002289},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3350813686847687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2987910509109497},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2874581217765808},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1286928355693817}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:digitalcommons.lib.uconn.edu:dissertations-1229","doi":null,"is_oa":false,"landing_page_url":"https://digitalcommons.lib.uconn.edu/dissertations/AAI9705030","pdf_url":null,"source":{"id":"https://openalex.org/S4377196438","display_name":"OpenCommons - UConn (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},{"id":"pmh:oai:opencommons.uconn.edu:dissertations-1229","doi":null,"is_oa":false,"landing_page_url":"http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:9705030","pdf_url":null,"source":{"id":"https://openalex.org/S4377196438","display_name":"OpenCommons - UConn (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},{"id":"mag:67644556","doi":null,"is_oa":false,"landing_page_url":"http://digitalcommons.uconn.edu/dissertations/AAI9705030","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":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7097257513","https://openalex.org/W7096781483","https://openalex.org/W137446006","https://openalex.org/W2030756250","https://openalex.org/W2134112533","https://openalex.org/W4318591473","https://openalex.org/W2736548716","https://openalex.org/W2041545409","https://openalex.org/W2092335024","https://openalex.org/W2086526810"],"abstract_inverted_index":{"In":[0],"the":[1,25,31,71,113,116,138,147,152,160,187,213,227,260,271,278,288,299],"conduct":[2],"of":[3,33,51,124,184,197,212,225,262,290],"sequential":[4,53],"clinical":[5],"trials,":[6],"primary":[7],"statistical":[8],"issues":[9],"include":[10],"design,":[11],"monitoring":[12],"and":[13,46,61,194,215,231,293],"reporting.":[14],"Currently,":[15],"approaches":[16],"built":[17],"upon":[18],"frequentist":[19],"inference":[20],"methodology":[21],"predominate.":[22],"Focusing":[23],"on":[24,137,287],"design":[26,55,178,214,221],"aspect,":[27],"our":[28,132,202],"objective":[29],"is":[30,100,171,179,238],"development":[32],"a":[34,52,57,59,62,67,83,90,103,122,174,177,182,195,205],"very":[35],"general":[36],"Bayesian":[37,54],"framework":[38],"permitting":[39],"multiple":[40,43,47,153],"arms":[41],"with":[42],"patient":[44,80],"endpoints":[45],"stopping":[48,63,154,161,198],"criteria.":[49],"Specification":[50],"requires":[56],"likelihood,":[58],"prior":[60,117,148],"rule.":[64,162],"We":[65,74,163,208,240,282],"take":[66],"nonparametric":[68],"perspective":[69],"for":[70,149,190,233,270],"likelihood":[72],"specification.":[73],"do":[75],"so":[76],"by":[77,181],"representing":[78],"discrete":[79],"response":[81],"as":[82,275,277],"categorical":[84],"outcome":[85],"described":[86],"through":[87,107],"cells":[88],"in":[89,115,223,259],"multiway":[91],"contingency":[92],"table,":[93],"while":[94],"we":[95,119,265],"assume":[96],"that":[97,150],"continuous":[98],"data":[99,135],"drawn":[101],"from":[102,126],"distribution":[104],"which":[105,200],"arises":[106],"Dirichlet":[108],"process":[109],"mixing.":[110],"To":[111],"address":[112],"subjectivity":[114],"specification":[118,176],"can":[120],"consider":[121],"range":[123],"priors":[125],"skeptical":[127],"to":[128,130,145,158,219,297],"enthusiastic":[129],"capture":[131],"views.":[133],"Historical":[134],"available":[136],"standard":[139],"or":[140],"placebo":[141],"arm":[142,151],"are":[143,156],"used":[144],"form":[146],"criteria":[155,199],"employed":[157],"define":[159],"propose":[164],"two":[165],"different":[166],"approaches:":[167],"The":[168,235],"first":[169],"one":[170],"descriptive.":[172],"Given":[173],"model":[175],"characterized":[180],"number":[183,261],"interim":[185,192,263],"evaluations,":[186],"group":[188],"size":[189],"each":[191],"look,":[193],"set":[196],"determine":[201],"decision":[203],"at":[204],"given":[206],"look.":[207],"then":[209,294],"simulate":[210],"replications":[211],"use":[216],"these":[217],"replicates":[218],"summarize":[220],"performance":[222],"terms":[224],"when":[226],"trial":[228],"was":[229],"stopped":[230],"reason":[232],"stopping.":[234],"second":[236],"approach":[237],"decision-theoretic.":[239],"obtain":[241],"optimal":[242],"bounded":[243],"designs":[244,292],"using":[245],"backward":[246],"induction,":[247],"implemented":[248],"via":[249],"Monte":[250],"Carlo":[251],"integration.":[252],"While":[253],"required":[254],"random":[255],"generation":[256],"grows":[257],"geometrically":[258],"looks":[264],"provide":[266],"computationally":[267],"feasible":[268],"bounds":[269],"needed":[272],"continuation":[273],"risks":[274],"well":[276],"expected":[279],"sample":[280],"sizes.":[281],"offer":[283],"some":[284],"qualitative":[285],"results":[286],"behavior":[289],"such":[291],"present":[295],"examples":[296],"illustrate":[298],"entire":[300],"development.":[301],"^":[302]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2016-06-24T00:00:00"}
