{"id":"https://openalex.org/W4294622447","doi":"https://doi.org/10.1080/03610918.2022.2118780","title":"Efficient estimation for accelerated failure time model with interval-censored data in the presence of a cured subgroup","display_name":"Efficient estimation for accelerated failure time model with interval-censored data in the presence of a cured subgroup","publication_year":2022,"publication_date":"2022-09-05","ids":{"openalex":"https://openalex.org/W4294622447","doi":"https://doi.org/10.1080/03610918.2022.2118780"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2022.2118780","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2118780","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/A5112988122","display_name":"Bo Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I4385474403","display_name":"Changchun University of Technology","ror":"https://ror.org/052pakb34","country_code":null,"type":"education","lineage":["https://openalex.org/I4385474403"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhao","raw_affiliation_strings":["School of Mathematics and Statistics, Changchun University of Technology, Changchun, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Changchun University of Technology, Changchun, China","institution_ids":["https://openalex.org/I4385474403","https://openalex.org/I4385474403"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100629910","display_name":"Shuying Wang","orcid":"https://orcid.org/0000-0002-7208-7227"},"institutions":[{"id":"https://openalex.org/I4385474403","display_name":"Changchun University of Technology","ror":"https://ror.org/052pakb34","country_code":null,"type":"education","lineage":["https://openalex.org/I4385474403"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuying Wang","raw_affiliation_strings":["School of Mathematics and Statistics, Changchun University of Technology, Changchun, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Changchun University of Technology, Changchun, China","institution_ids":["https://openalex.org/I4385474403","https://openalex.org/I4385474403"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100668506","display_name":"Chunjie Wang","orcid":"https://orcid.org/0000-0001-7808-4900"},"institutions":[{"id":"https://openalex.org/I4385474403","display_name":"Changchun University of Technology","ror":"https://ror.org/052pakb34","country_code":null,"type":"education","lineage":["https://openalex.org/I4385474403"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunjie Wang","raw_affiliation_strings":["School of Mathematics and Statistics, Changchun University of Technology, Changchun, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Changchun University of Technology, Changchun, China","institution_ids":["https://openalex.org/I4385474403","https://openalex.org/I4385474403"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100629910"],"corresponding_institution_ids":["https://openalex.org/I4385474403"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09787806,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"53","issue":"8","first_page":"3965","last_page":"3977"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9972000122070312,"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":0.9972000122070312,"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":0.9901999831199646,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9800999760627747,"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/accelerated-failure-time-model","display_name":"Accelerated failure time model","score":0.786307156085968},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6095237135887146},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.5986928343772888},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5695924162864685},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.564753532409668},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5339580774307251},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5153457522392273},{"id":"https://openalex.org/keywords/semiparametric-regression","display_name":"Semiparametric regression","score":0.47026023268699646},{"id":"https://openalex.org/keywords/logarithm","display_name":"Logarithm","score":0.46619147062301636},{"id":"https://openalex.org/keywords/proportional-hazards-model","display_name":"Proportional hazards model","score":0.45607832074165344},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3951745331287384},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3503321707248688}],"concepts":[{"id":"https://openalex.org/C33114746","wikidata":"https://www.wikidata.org/wiki/Q4672282","display_name":"Accelerated failure time model","level":3,"score":0.786307156085968},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6095237135887146},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5986928343772888},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5695924162864685},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.564753532409668},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5339580774307251},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5153457522392273},{"id":"https://openalex.org/C19539793","wikidata":"https://www.wikidata.org/wiki/Q7449609","display_name":"Semiparametric regression","level":3,"score":0.47026023268699646},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.46619147062301636},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.45607832074165344},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3951745331287384},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3503321707248688},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2022.2118780","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2118780","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/16","display_name":"Peace, Justice and strong institutions","score":0.5699999928474426}],"awards":[{"id":"https://openalex.org/G1142510305","display_name":null,"funder_award_id":"11671054","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3871318577","display_name":null,"funder_award_id":"2021M700536","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G838054019","display_name":null,"funder_award_id":"11901054","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W573849129","https://openalex.org/W1571146896","https://openalex.org/W1964136476","https://openalex.org/W1986986835","https://openalex.org/W2003951390","https://openalex.org/W2005532752","https://openalex.org/W2010353172","https://openalex.org/W2018670982","https://openalex.org/W2029115180","https://openalex.org/W2037629605","https://openalex.org/W2050786196","https://openalex.org/W2051305724","https://openalex.org/W2061893665","https://openalex.org/W2064207173","https://openalex.org/W2064376741","https://openalex.org/W2079559497","https://openalex.org/W2084696120","https://openalex.org/W2091745168","https://openalex.org/W2092338474","https://openalex.org/W2134902673","https://openalex.org/W2141589372","https://openalex.org/W2153135454","https://openalex.org/W2158436837","https://openalex.org/W2275112306","https://openalex.org/W2342834172","https://openalex.org/W2360906891","https://openalex.org/W2524284946","https://openalex.org/W2623593966","https://openalex.org/W2765994858","https://openalex.org/W2795085959","https://openalex.org/W2797482815","https://openalex.org/W2977606681","https://openalex.org/W2989745679","https://openalex.org/W3122767931","https://openalex.org/W3135109640","https://openalex.org/W3159522096","https://openalex.org/W3175489983","https://openalex.org/W4292025355","https://openalex.org/W6707447294","https://openalex.org/W7026736108"],"related_works":["https://openalex.org/W2099008555","https://openalex.org/W2038710811","https://openalex.org/W3084273290","https://openalex.org/W1480625639","https://openalex.org/W87401070","https://openalex.org/W2380754093","https://openalex.org/W3132242480","https://openalex.org/W2907763160","https://openalex.org/W3146361383","https://openalex.org/W4308165859"],"abstract_inverted_index":{"As":[0],"the":[1,6,15,18,22,28,44,50,57,67,71,83,89,102,119,126,133,136,139,146,157,175],"alternative":[2],"of":[3,17,30,52,86,135,138,145,174],"Cox":[4],"model,":[5,11],"accelerated":[7,77],"failure":[8,46,78,84],"time":[9,20,47,79,85],"(AFT)":[10],"which":[12],"simply":[13],"regresses":[14],"logarithm":[16],"survival":[19],"over":[21],"covariates,":[23],"is":[24,60,74,96,170],"commonly":[25],"used":[26],"in":[27,49,101],"analysis":[29,169],"interval-censored":[31,45],"data.":[32],"In":[33,165],"this":[34],"paper,":[35],"we":[36],"propose":[37,106],"a":[38,53,61,75,107],"novel":[39],"two-component":[40],"mixture-cure":[41],"model":[42,64,80],"for":[43,88,162,172],"data":[48,168],"presence":[51],"cure":[54,68],"fraction.":[55],"Specifically,":[56],"first":[58],"component":[59,73],"logistic":[62],"regression":[63,120],"that":[65,81,156],"describes":[66,82],"rate,":[69],"and":[70,123,125],"second":[72],"semiparametric":[76,94],"interest":[87],"uncured":[90],"subjects.":[91],"An":[92],"efficient":[93],"procedure":[95,128,159],"developed":[97],"to":[98,117],"estimate":[99,118],"parameters":[100,121],"considered":[103],"model.":[104],"We":[105],"penalized":[108],"sieve":[109],"maximum":[110],"likelihood":[111],"estimation":[112],"approach":[113],"with":[114],"Bernstein":[115],"polynomials":[116],"quickly":[122],"accurately":[124],"proposed":[127,158,176],"does":[129],"not":[130],"rely":[131],"on":[132],"assumption":[134],"distribution":[137],"measurement":[140],"error.":[141],"The":[142],"asymptotic":[143],"properties":[144],"resulting":[147],"estimators":[148],"are":[149],"established.":[150],"Extensive":[151],"simulation":[152],"studies":[153],"conducted":[154],"indicate":[155],"works":[160],"well":[161],"practical":[163],"situations.":[164],"addition,":[166],"AIDS":[167],"provided":[171],"illustration":[173],"method.":[177]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
