{"id":"https://openalex.org/W2962644201","doi":"https://doi.org/10.1080/03610918.2019.1636998","title":"Subgroup analysis of censored data on cancer treatment","display_name":"Subgroup analysis of censored data on cancer treatment","publication_year":2019,"publication_date":"2019-07-09","ids":{"openalex":"https://openalex.org/W2962644201","doi":"https://doi.org/10.1080/03610918.2019.1636998","mag":"2962644201"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2019.1636998","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1636998","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","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/A5100358791","display_name":"Qing Zhang","orcid":"https://orcid.org/0000-0002-1987-1477"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qing Zhang","raw_affiliation_strings":["Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008454216","display_name":"Hongshik Ahn","orcid":"https://orcid.org/0000-0002-7706-3320"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hongshik Ahn","raw_affiliation_strings":["Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008454216"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09641825,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":"12","first_page":"4041","last_page":"4058"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9886999726295471,"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":0.9886999726295471,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9693999886512756,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9578999876976013,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6302525401115417},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5839129686355591},{"id":"https://openalex.org/keywords/bootstrap-aggregating","display_name":"Bootstrap aggregating","score":0.5437525510787964},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5237522125244141},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47061851620674133},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.4569050371646881},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3936326503753662},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39315155148506165},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3636184334754944},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32953962683677673},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07257208228111267}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6302525401115417},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5839129686355591},{"id":"https://openalex.org/C162040801","wikidata":"https://www.wikidata.org/wiki/Q799897","display_name":"Bootstrap aggregating","level":2,"score":0.5437525510787964},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5237522125244141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47061851620674133},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.4569050371646881},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3936326503753662},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39315155148506165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3636184334754944},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32953962683677673},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07257208228111267},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2019.1636998","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1636998","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.5,"display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G1424279823","display_name":null,"funder_award_id":"IITP-2017-R0346-15-1007","funder_id":"https://openalex.org/F4320322030","funder_display_name":"Ministry of Science, ICT and Future Planning"}],"funders":[{"id":"https://openalex.org/F4320322030","display_name":"Ministry of Science, ICT and Future Planning","ror":"https://ror.org/032e49973"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W116090520","https://openalex.org/W1566001373","https://openalex.org/W1580788756","https://openalex.org/W1594031697","https://openalex.org/W1978250117","https://openalex.org/W1978391225","https://openalex.org/W1982637896","https://openalex.org/W2107296273","https://openalex.org/W2145878099","https://openalex.org/W2149860264","https://openalex.org/W4238685868"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W1985727224","https://openalex.org/W3116434045","https://openalex.org/W2050617434"],"abstract_inverted_index":{"We":[0,48,101],"develop":[1],"some":[2],"statistical":[3],"methods":[4],"based":[5,138],"on":[6,81,126,139],"tree-structured":[7],"classification":[8],"to":[9,26,35,83,92],"assign":[10],"patients":[11,39],"from":[12,146,153],"different":[13,19,105],"treatment":[14,43,191],"groups":[15],"into":[16,53],"subgroups":[17,37],"with":[18,159],"medical":[20,109],"recommendations.":[21,110],"Since":[22],"it":[23],"is":[24,116,137,193],"difficult":[25],"discover":[27],"treatments":[28],"that":[29],"benefit":[30],"all":[31,169],"patients,":[32],"we":[33,77],"want":[34],"identify":[36,84],"of":[38,62,73,86,107,112,134,143,174],"for":[40],"whom":[41],"the":[42,58,63,70,87,99,113,135,141,150,154,172,178,186,190],"has":[44],"an":[45],"enhanced":[46],"effect.":[47],"classify":[49],"each":[50,74],"terminal":[51,75],"node":[52,64],"a":[54,79],"subgroup":[55,72,94],"by":[56,95],"comparing":[57,140],"relative":[59],"event":[60],"rates":[61],"and":[65,122,149],"its":[66],"immediate":[67],"predecessor.":[68],"Given":[69],"suggested":[71],"node,":[76],"propose":[78,103],"method":[80,115],"how":[82],"which":[85,93],"splitting":[88],"variables":[89],"are":[90],"keen":[91],"tracing":[96],"back":[97],"along":[98],"tree.":[100],"also":[102],"two":[104],"ways":[106],"assigning":[108],"Performance":[111],"proposed":[114],"evaluated":[117],"using":[118,123],"bagging":[119,147,166,175,183],"multiple":[120],"trees,":[121],"cross":[124],"validations":[125],"single":[127],"trees":[128,148,158,167,176,184],"as":[129],"our":[130],"benchmark":[131],"model.":[132],"Evaluation":[133],"results":[136],"out":[142],"bag":[144],"accuracy":[145,152],"test":[151],"cross-validated":[155],"trees.":[156],"Bagging":[157],"randomly":[160],"selected":[161],"features":[162,170],"perform":[163],"better":[164],"than":[165],"including":[168],"when":[171,189],"number":[173],"exceeds":[177],"necessary":[179],"ntree":[180],"number.":[181],"The":[182],"outperform":[185],"benchmark,":[187],"especially":[188],"effect":[192],"more":[194],"homogeneous.":[195]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
