{"id":"https://openalex.org/W7204170443","doi":"https://doi.org/10.1080/02664763.2026.2721766","title":"Bayesian survival analysis for high-Dimensional compositional data","display_name":"Bayesian survival analysis for high-Dimensional compositional data","publication_year":2026,"publication_date":"2026-08-25","ids":{"openalex":"https://openalex.org/W7204170443","doi":"https://doi.org/10.1080/02664763.2026.2721766"},"language":"en","primary_location":{"id":"doi:10.1080/02664763.2026.2721766","doi":"https://doi.org/10.1080/02664763.2026.2721766","is_oa":false,"landing_page_url":"https://doi.org/10.1080/02664763.2026.2721766","pdf_url":null,"source":{"id":"https://openalex.org/S167117542","display_name":"Journal of Applied Statistics","issn_l":"0266-4763","issn":["0266-4763","1360-0532"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","cwts-core","doyens","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Applied Statistics","raw_type":"journal-article"},"volume":null,"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/A5125633067","display_name":"Zhenying Ding","orcid":"https://orcid.org/0009-0004-6111-5730","observed_orcids":["https://orcid.org/0009-0004-6111-5730"]},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhenying Ding","raw_affiliation_strings":["University of Alabama at Birmingham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama at Birmingham","institution_ids":["https://openalex.org/I32389192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148099087","display_name":"Li Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1289437631","display_name":"Fox Chase Cancer Center","ror":"https://ror.org/0567t7073","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1289437631","https://openalex.org/I4210122924"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Fox Chase Cancer Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fox Chase Cancer Center","institution_ids":["https://openalex.org/I1289437631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042591595","display_name":"Satwik Acharyya","orcid":"https://orcid.org/0000-0003-2660-9781","observed_orcids":["https://orcid.org/0000-0003-2660-9781"]},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Satwik Acharyya","raw_affiliation_strings":["University of Alabama at Birmingham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama at Birmingham","institution_ids":["https://openalex.org/I32389192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148081302","display_name":"Peng Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Li","raw_affiliation_strings":["University of Alabama at Birmingham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama at Birmingham","institution_ids":["https://openalex.org/I32389192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101814115","display_name":"AKM Fazlur Rahman","orcid":"https://orcid.org/0000-0002-7275-1992","observed_orcids":["https://orcid.org/0000-0002-7275-1992"]},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"AKM Fazlur Rahman","raw_affiliation_strings":["University of Alabama at Birmingham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama at Birmingham","institution_ids":["https://openalex.org/I32389192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101054437","display_name":"Xiaoxiao Zhou","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoxiao Zhou","raw_affiliation_strings":["University of Alabama at Birmingham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama at Birmingham","institution_ids":["https://openalex.org/I32389192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086059667","display_name":"Nengjun Yi","orcid":"https://orcid.org/0000-0001-5857-5868","observed_orcids":["https://orcid.org/0000-0001-5857-5868"]},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nengjun Yi","raw_affiliation_strings":["University of Alabama at Birmingham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama at Birmingham","institution_ids":["https://openalex.org/I32389192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086059667","https://openalex.org/A5125633067"],"corresponding_institution_ids":["https://openalex.org/I32389192"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71235245,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.6625999808311462,"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":0.6625999808311462,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.06759999692440033,"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/T13500","display_name":"Probability and Statistical Research","score":0.042399998754262924,"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-survival-analysis","display_name":"Bayesian survival analysis","score":0.6539999842643738},{"id":"https://openalex.org/keywords/compositional-data","display_name":"compositional data","score":0.8669999837875366},{"id":"https://openalex.org/keywords/microbiome","display_name":"microbiome","score":0.9599999785423279},{"id":"https://openalex.org/keywords/cox-proportional-hazards-model","display_name":"Cox proportional hazards model","score":0.9769999980926514},{"id":"https://openalex.org/keywords/horseshoe-prior","display_name":"horseshoe prior","score":0.8450000286102295},{"id":"https://openalex.org/keywords/host-microbe-interactions","display_name":"host-microbe interactions","score":0.8830000162124634},{"id":"https://openalex.org/keywords/biomarkers","display_name":"biomarkers","score":0.6710000038146973},{"id":"https://openalex.org/keywords/high-dimensional-data","display_name":"high-dimensional data","score":0.5839999914169312}],"concepts":[{"id":"https://openalex.org/C143121216","wikidata":"https://www.wikidata.org/wiki/Q1330402","display_name":"Microbiome","level":2,"score":0.6996999979019165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6488999724388123},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5820000171661377},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5627999901771545},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.4957999885082245},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46480000019073486},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.4596000015735626},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.39750000834465027},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3871999979019165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3808000087738037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37279999256134033},{"id":"https://openalex.org/C193252679","wikidata":"https://www.wikidata.org/wiki/Q242125","display_name":"Phylogenetic tree","level":3,"score":0.36059999465942383},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C33114746","wikidata":"https://www.wikidata.org/wiki/Q4672282","display_name":"Accelerated failure time model","level":3,"score":0.3253999948501587},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C91478284","wikidata":"https://www.wikidata.org/wiki/Q6623796","display_name":"Human microbiome","level":3,"score":0.31450000405311584},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.31150001287460327},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C2993206177","wikidata":"https://www.wikidata.org/wiki/Q2844457","display_name":"Amplicon sequencing","level":4,"score":0.288100004196167},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C2781147490","wikidata":"https://www.wikidata.org/wiki/Q5156808","display_name":"Compositional data","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C201797286","wikidata":"https://www.wikidata.org/wiki/Q4914986","display_name":"Biological data","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/02664763.2026.2721766","doi":"https://doi.org/10.1080/02664763.2026.2721766","is_oa":false,"landing_page_url":"https://doi.org/10.1080/02664763.2026.2721766","pdf_url":null,"source":{"id":"https://openalex.org/S167117542","display_name":"Journal of Applied Statistics","issn_l":"0266-4763","issn":["0266-4763","1360-0532"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","cwts-core","doyens","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Applied Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7246000170707703,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"sustainable_development_goals_aurora":[{"score":0.4272405803203583,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1480172732","https://openalex.org/W2029607409","https://openalex.org/W2120288828","https://openalex.org/W2125230147","https://openalex.org/W2131415145","https://openalex.org/W2149199519","https://openalex.org/W2170024099","https://openalex.org/W2250012816","https://openalex.org/W2731461838","https://openalex.org/W2748709680","https://openalex.org/W2769542288","https://openalex.org/W2811198907","https://openalex.org/W2904250115","https://openalex.org/W2962797912","https://openalex.org/W3042363243","https://openalex.org/W3147894994","https://openalex.org/W3204768538","https://openalex.org/W4388850904"],"related_works":[],"abstract_inverted_index":{"Survival":[0],"analysis":[1,153],"integrating":[2],"microbiome":[3,86,107,118,135],"and":[4,23,48,60,80,108,157],"clinical":[5,17,109],"data":[6],"offers":[7],"powerful":[8],"insights":[9],"into":[10],"human":[11],"health.":[12],"Combining":[13],"microbial":[14],"profiles":[15],"with":[16,100,122,171],"factors":[18],"like":[19],"tumor":[20],"stage,":[21],"treatment,":[22],"demographics":[24],"enhances":[25],"understanding":[26],"of":[27,134,147],"how":[28],"host-microbe":[29],"interactions":[30],"affect":[31],"survival.":[32],"To":[33,142],"analyze":[34],"these":[35],"complex":[36],"datasets,":[37],"advanced":[38],"statistical":[39],"methods,":[40],"including":[41],"Bayesian":[42,94],"models,":[43,47],"Cox":[44,96],"proportional":[45,97],"hazards":[46,98],"machine":[49],"learning":[50],"techniques":[51],"are":[52],"employed.":[53],"These":[54],"approaches":[55],"can":[56],"uncover":[57],"novel":[58],"biomarkers":[59],"therapeutic":[61],"targets,":[62],"leading":[63],"to":[64,76,117,120,137],"personalized":[65],"treatment":[66],"strategies":[67],"that":[68],"optimize":[69],"patient":[70],"outcome.":[71],"However,":[72],"challenges":[73],"remain":[74],"due":[75],"the":[77,132,139,144,167],"compositionality,":[78],"high-dimensionality,":[79],"phylogenetic":[81,140],"relation":[82],"between":[83],"taxa":[84,136],"in":[85,184],"data.":[87,110],"In":[88],"this":[89],"paper,":[90],"we":[91,125,150],"proposed":[92],"a":[93,101,113,127,158],"compositional":[95,106],"model":[99],"regularized":[102],"horseshoe":[103],"prior":[104,130],"analyzing":[105],"We":[111],"applied":[112],"soft":[114],"sum-to-zero":[115],"constraint":[116],"coefficients":[119],"deal":[121],"compositionality.":[123],"Also,":[124],"introduced":[126],"structured":[128],"shrinkage":[129],"incorporating":[131],"similarity":[133],"address":[138],"structure.":[141],"evaluate":[143],"predictive":[145],"performance":[146],"our":[148],"model,":[149],"conducted":[151],"an":[152],"on":[154,175],"extensive":[155],"simulations":[156],"real":[159],"dataset.":[160],"The":[161],"implementation":[162],"was":[163],"carried":[164],"out":[165],"using":[166],"R":[168],"package":[169],"brms,":[170],"results":[172],"summarized":[173],"based":[174],"two":[176],"Markov":[177],"Chain":[178],"Monte":[179],"Carlo":[180],"(MCMC)":[181],"algorithms":[182],"executed":[183],"Stan.":[185]},"counts_by_year":[],"updated_date":"2026-09-25T08:18:29.962427","created_date":"2026-08-26T00:00:00"}
