{"id":"https://openalex.org/W7130642412","doi":"https://doi.org/10.3390/axioms15020155","title":"Testing Homogeneity of Odds Ratio for Stratified Bilateral Correlated Data","display_name":"Testing Homogeneity of Odds Ratio for Stratified Bilateral Correlated Data","publication_year":2026,"publication_date":"2026-02-20","ids":{"openalex":"https://openalex.org/W7130642412","doi":"https://doi.org/10.3390/axioms15020155"},"language":"en","primary_location":{"id":"doi:10.3390/axioms15020155","is_oa":true,"landing_page_url":"https://doi.org/10.3390/axioms15020155","pdf_url":"https://www.mdpi.com/2075-1680/15/2/155/pdf","source":{"id":"https://openalex.org/S4210173132","display_name":"Axioms","issn_l":"2075-1680","issn":["2075-1680"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Axioms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2075-1680/15/2/155/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126473592","display_name":"Xi Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xi Shen","raw_affiliation_strings":["Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY 14214, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY 14214, USA","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126450210","display_name":"Xueqing Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueqing Zhang","raw_affiliation_strings":["Department of Statistics, Yanshan University, Qinhuangdao 066004, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Yanshan University, Qinhuangdao 066004, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112195738","display_name":"Chang-Xing Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chang-Xing Ma","raw_affiliation_strings":["Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY 14214, USA"],"raw_orcid":"https://orcid.org/0000-0002-4696-2008","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY 14214, USA","institution_ids":["https://openalex.org/I63190737"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112195738"],"corresponding_institution_ids":["https://openalex.org/I63190737"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1782},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21182813,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":"2","first_page":"155","last_page":"155"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.4235999882221222,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.4235999882221222,"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/T13309","display_name":"Reliability and Agreement in Measurement","score":0.11089999973773956,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.10610000044107437,"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/homogeneity","display_name":"Homogeneity (statistics)","score":0.6488000154495239},{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.6363999843597412},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.512499988079071},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.48100000619888306},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.43369999527931213},{"id":"https://openalex.org/keywords/type-i-and-type-ii-errors","display_name":"Type I and type II errors","score":0.4327000081539154},{"id":"https://openalex.org/keywords/odds","display_name":"Odds","score":0.41440001130104065}],"concepts":[{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6855000257492065},{"id":"https://openalex.org/C142259097","wikidata":"https://www.wikidata.org/wiki/Q5891314","display_name":"Homogeneity (statistics)","level":2,"score":0.6488000154495239},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.6363999843597412},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.48100000619888306},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C143095724","wikidata":"https://www.wikidata.org/wiki/Q515895","display_name":"Odds","level":3,"score":0.41440001130104065},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3774999976158142},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3402000069618225},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3375000059604645},{"id":"https://openalex.org/C183905921","wikidata":"https://www.wikidata.org/wiki/Q1038757","display_name":"Multiple comparisons problem","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2969000041484833},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C156957248","wikidata":"https://www.wikidata.org/wiki/Q1862216","display_name":"Odds ratio","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.2603999972343445}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/axioms15020155","is_oa":true,"landing_page_url":"https://doi.org/10.3390/axioms15020155","pdf_url":"https://www.mdpi.com/2075-1680/15/2/155/pdf","source":{"id":"https://openalex.org/S4210173132","display_name":"Axioms","issn_l":"2075-1680","issn":["2075-1680"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Axioms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4d6379faeb964117929a4f9b3b137361","is_oa":false,"landing_page_url":"https://doaj.org/article/4d6379faeb964117929a4f9b3b137361","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"Axioms, Vol 15, Iss 2, p 155 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/axioms15020155","is_oa":true,"landing_page_url":"https://doi.org/10.3390/axioms15020155","pdf_url":"https://www.mdpi.com/2075-1680/15/2/155/pdf","source":{"id":"https://openalex.org/S4210173132","display_name":"Axioms","issn_l":"2075-1680","issn":["2075-1680"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Axioms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7130642412.pdf","grobid_xml":"https://content.openalex.org/works/W7130642412.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,66,94],"clinical":[1],"studies":[2,125],"such":[3,28],"as":[4],"ophthalmologic":[5],"or":[6,21,72],"otolaryngologic":[7],"research,":[8],"bilateral":[9,48,111],"correlated":[10,112],"data":[11,36,49,92,113,183],"frequently":[12],"arise":[13],"when":[14],"outcomes":[15],"are":[16,31,126,168,177],"collected":[17],"from":[18,27],"paired":[19,29],"organs":[20],"body":[22],"parts.":[23],"Since":[24],"the":[25,41,55,69,91,104,115,130,133,140,149],"measurements":[26],"observations":[30],"usually":[32],"highly":[33],"correlated,":[34],"appropriate":[35],"analysis":[37],"requires":[38],"accounting":[39],"for":[40,46,87,102,171],"intra-class":[42],"correlation.":[43],"Methodological":[44],"developments":[45],"analyzing":[47],"have":[50],"been":[51],"extensively":[52],"studied":[53],"over":[54],"past":[56],"several":[57],"decades,":[58],"including":[59],"both":[60],"inferential":[61],"procedures":[62,101],"and":[63,148,158,167],"computational":[64],"strategies.":[65],"some":[67],"analyses,":[68],"center":[70],"effect":[71,74],"confounding":[73],"could":[75],"lead":[76],"to":[77,85,128],"imbalance":[78],"among":[79],"treatment":[80],"arms,":[81],"making":[82],"it":[83],"necessary":[84],"adjust":[86],"stratification/confounding":[88],"factors":[89],"in":[90,109],"analysis.":[93],"this":[95],"article,":[96],"we":[97],"develop":[98],"three":[99],"testing":[100],"assessing":[103],"homogeneity":[105],"of":[106,117,132,165],"odds":[107],"ratios":[108],"stratified":[110],"under":[114],"assumption":[116],"a":[118,145,163],"common":[119],"correlation":[120],"structure.":[121],"Monte":[122],"Carlo":[123],"simulation":[124],"conducted":[127],"evaluate":[129],"performance":[131],"proposed":[134,175],"methods.":[135],"The":[136,174],"results":[137],"indicate":[138],"that":[139],"Wald-type":[141],"test":[142,151],"based":[143],"on":[144],"log-linear":[146],"hypothesis":[147],"score":[150],"maintain":[152],"robust":[153],"type":[154],"I":[155],"error":[156],"rates":[157],"achieve":[159],"high":[160],"power":[161],"across":[162],"range":[164],"scenarios,":[166],"therefore":[169],"recommended":[170],"practical":[172],"application.":[173],"methodologies":[176],"further":[178],"illustrated":[179],"using":[180],"two":[181],"real":[182],"examples.":[184]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-02-20T00:00:00"}