{"id":"https://openalex.org/W3195581570","doi":"https://doi.org/10.1007/s11634-021-00458-3","title":"Subgroup identification in individual participant data meta-analysis using model-based recursive partitioning","display_name":"Subgroup identification in individual participant data meta-analysis using model-based recursive partitioning","publication_year":2021,"publication_date":"2021-08-14","ids":{"openalex":"https://openalex.org/W3195581570","doi":"https://doi.org/10.1007/s11634-021-00458-3","mag":"3195581570"},"language":"en","primary_location":{"id":"doi:10.1007/s11634-021-00458-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11634-021-00458-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11634-021-00458-3.pdf","source":{"id":"https://openalex.org/S4210175730","display_name":"Advances in Data Analysis and Classification","issn_l":"1862-5347","issn":["1862-5347","1862-5355"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Data Analysis and Classification","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11634-021-00458-3.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061539702","display_name":"Cynthia Huber","orcid":"https://orcid.org/0000-0003-2035-3682"},"institutions":[{"id":"https://openalex.org/I4210116730","display_name":"Universit\u00e4tsmedizin G\u00f6ttingen","ror":"https://ror.org/021ft0n22","country_code":"DE","type":"funder","lineage":["https://openalex.org/I4210116730"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Cynthia Huber","raw_affiliation_strings":["Department of Medical Statistics, University Medical Center G\u00f6ttingen, G\u00f6ttingen, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Medical Statistics, University Medical Center G\u00f6ttingen, G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I4210116730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008233479","display_name":"Norbert Benda","orcid":"https://orcid.org/0000-0001-5605-2414"},"institutions":[{"id":"https://openalex.org/I123859907","display_name":"Federal Institute for Drugs and Medical Devices","ror":"https://ror.org/05ex5vz81","country_code":"DE","type":"funder","lineage":["https://openalex.org/I123859907"]},{"id":"https://openalex.org/I4210116730","display_name":"Universit\u00e4tsmedizin G\u00f6ttingen","ror":"https://ror.org/021ft0n22","country_code":"DE","type":"funder","lineage":["https://openalex.org/I4210116730"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Norbert Benda","raw_affiliation_strings":["Department of Medical Statistics, University Medical Center G\u00f6ttingen, G\u00f6ttingen, Germany","Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Medical Statistics, University Medical Center G\u00f6ttingen, G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I4210116730"]},{"raw_affiliation_string":"Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany","institution_ids":["https://openalex.org/I123859907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057325416","display_name":"Tim Friede","orcid":"https://orcid.org/0000-0001-5347-7441"},"institutions":[{"id":"https://openalex.org/I4210116730","display_name":"Universit\u00e4tsmedizin G\u00f6ttingen","ror":"https://ror.org/021ft0n22","country_code":"DE","type":"funder","lineage":["https://openalex.org/I4210116730"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tim Friede","raw_affiliation_strings":["Department of Medical Statistics, University Medical Center G\u00f6ttingen, G\u00f6ttingen, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Medical Statistics, University Medical Center G\u00f6ttingen, G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I4210116730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061539702"],"corresponding_institution_ids":["https://openalex.org/I4210116730"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.9465,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77950732,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"16","issue":"3","first_page":"797","last_page":"815"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9975000023841858,"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":0.9975000023841858,"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/T10206","display_name":"Meta-analysis and systematic reviews","score":0.9876000285148621,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9760000109672546,"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/recursive-partitioning","display_name":"Recursive partitioning","score":0.8638894557952881},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.7074266076087952},{"id":"https://openalex.org/keywords/random-effects-model","display_name":"Random effects model","score":0.6659952402114868},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.604488730430603},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5898709297180176},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5532559156417847},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.49427366256713867},{"id":"https://openalex.org/keywords/mixed-model","display_name":"Mixed model","score":0.493415504693985},{"id":"https://openalex.org/keywords/generalized-linear-mixed-model","display_name":"Generalized linear mixed model","score":0.4913133680820465},{"id":"https://openalex.org/keywords/type-i-and-type-ii-errors","display_name":"Type I and type II errors","score":0.45607948303222656},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4325619339942932},{"id":"https://openalex.org/keywords/meta-analysis","display_name":"Meta-analysis","score":0.42648255825042725},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.41908878087997437},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.41637134552001953},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4146435260772705},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.39614710211753845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2761644423007965},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.17252758145332336}],"concepts":[{"id":"https://openalex.org/C137345334","wikidata":"https://www.wikidata.org/wiki/Q7303350","display_name":"Recursive partitioning","level":2,"score":0.8638894557952881},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.7074266076087952},{"id":"https://openalex.org/C168743327","wikidata":"https://www.wikidata.org/wiki/Q1826427","display_name":"Random effects model","level":3,"score":0.6659952402114868},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.604488730430603},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5898709297180176},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5532559156417847},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.49427366256713867},{"id":"https://openalex.org/C16012445","wikidata":"https://www.wikidata.org/wiki/Q1501135","display_name":"Mixed model","level":2,"score":0.493415504693985},{"id":"https://openalex.org/C153720581","wikidata":"https://www.wikidata.org/wiki/Q5532490","display_name":"Generalized linear mixed model","level":2,"score":0.4913133680820465},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.45607948303222656},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4325619339942932},{"id":"https://openalex.org/C95190672","wikidata":"https://www.wikidata.org/wiki/Q815382","display_name":"Meta-analysis","level":2,"score":0.42648255825042725},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.41908878087997437},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.41637134552001953},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4146435260772705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39614710211753845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2761644423007965},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.17252758145332336},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1007/s11634-021-00458-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11634-021-00458-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11634-021-00458-3.pdf","source":{"id":"https://openalex.org/S4210175730","display_name":"Advances in Data Analysis and Classification","issn_l":"1862-5347","issn":["1862-5347","1862-5355"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Data Analysis and Classification","raw_type":"journal-article"},{"id":"pmh:oai:publications.goettingen-research-online.de:2/123343","is_oa":true,"landing_page_url":"https://resolver.sub.uni-goettingen.de/purl?gro-2/123343","pdf_url":null,"source":{"id":"https://openalex.org/S4306401634","display_name":"GoeScholar  The Publication Server of the Georg-August-Universit\u00e4t G\u00f6ttingen (Georg-August-Universit\u00e4t G\u00f6ttingen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210122495","host_organization_name":"Asklepios Klinik St. Georg","host_organization_lineage":["https://openalex.org/I4210122495"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:arXiv.org:2009.10518","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.10518","pdf_url":"https://arxiv.org/pdf/2009.10518","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:RePEc:spr:advdac:v:16:y:2022:i:3:d:10.1007_s11634-021-00458-3","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s11634-021-00458-3","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s11634-021-00458-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11634-021-00458-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11634-021-00458-3.pdf","source":{"id":"https://openalex.org/S4210175730","display_name":"Advances in Data Analysis and Classification","issn_l":"1862-5347","issn":["1862-5347","1862-5355"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Data Analysis and Classification","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320317499","display_name":"Bundesinstitut f\u00fcr Arzneimittel und Medizinprodukte","ror":"https://ror.org/05ex5vz81"},{"id":"https://openalex.org/F4320318756","display_name":"Universit\u00e4tsmedizin G\u00f6ttingen","ror":"https://ror.org/021ft0n22"},{"id":"https://openalex.org/F4320321870","display_name":"Georg-August-Universit\u00e4t G\u00f6ttingen","ror":"https://ror.org/01y9bpm73"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3195581570.pdf","grobid_xml":"https://content.openalex.org/works/W3195581570.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W69784543","https://openalex.org/W91108901","https://openalex.org/W1976215007","https://openalex.org/W2016603580","https://openalex.org/W2045803758","https://openalex.org/W2067411780","https://openalex.org/W2081738310","https://openalex.org/W2124839051","https://openalex.org/W2130939988","https://openalex.org/W2146962227","https://openalex.org/W2156562757","https://openalex.org/W2173572484","https://openalex.org/W2216356458","https://openalex.org/W2271307894","https://openalex.org/W2322461341","https://openalex.org/W2339147944","https://openalex.org/W2396610607","https://openalex.org/W2476265589","https://openalex.org/W2503369342","https://openalex.org/W2582559731","https://openalex.org/W2582743722","https://openalex.org/W2616848457","https://openalex.org/W2713480577","https://openalex.org/W2772798561","https://openalex.org/W2780413079","https://openalex.org/W2783147278","https://openalex.org/W2803606087","https://openalex.org/W2886356254","https://openalex.org/W2952822714","https://openalex.org/W2954114517","https://openalex.org/W3003066324","https://openalex.org/W3100189218","https://openalex.org/W3100282936","https://openalex.org/W3122065848"],"related_works":["https://openalex.org/W4239710992","https://openalex.org/W2895736713","https://openalex.org/W1551599973","https://openalex.org/W4307324678","https://openalex.org/W2082190293","https://openalex.org/W2021312236","https://openalex.org/W4289422681","https://openalex.org/W225107685","https://openalex.org/W1988055263","https://openalex.org/W2953058310"],"abstract_inverted_index":{"Abstract":[0],"Model-based":[1],"recursive":[2,192],"partitioning":[3,193],"(MOB)":[4],"can":[5,43],"be":[6],"used":[7],"to":[8,83,205],"identify":[9],"subgroups":[10,26,65,213],"with":[11,159,203,230],"differing":[12],"treatment":[13,103,218],"effects.":[14,124],"The":[15,220],"detection":[16],"rate":[17],"of":[18,24,47,63,76,151,211,216],"treatment-by-covariate":[19],"interactions":[20],"and":[21,73,190,214,223],"the":[22,32,45,61,102,113,134,149,198,206,235,239],"accuracy":[23,210,215],"identified":[25,212],"using":[27,107,133],"MOB":[28],"depend":[29],"strongly":[30],"on":[31,172],"sample":[33],"size.":[34],"Using":[35],"data":[36,54,91],"from":[37,55],"multiple":[38,56],"randomized":[39,157,173],"controlled":[40,174],"clinical":[41,175],"trials":[42,57],"overcome":[44],"problem":[46],"too":[48],"small":[49],"samples.":[50],"However,":[51],"naively":[52],"pooling":[53],"may":[58],"result":[59],"in":[60,68,88,101,112,145,161,167,238],"identification":[62,144],"spurious":[64],"as":[66],"differences":[67],"study":[69],"design,":[70],"subject":[71],"selection":[72],"other":[74],"sources":[75],"between-trial":[77,86,236],"heterogeneity":[78,87,100,111,237],"are":[79,96],"ignored.":[80],"In":[81,125,177],"order":[82],"account":[84],"for":[85,142,200,233],"individual":[89],"participant":[90],"(IPD)":[92],"meta-analysis":[93],"random-effect":[94],"models":[95],"frequently":[97],"used.":[98],"Commonly,":[99],"effect":[104],"is":[105,116,153,197,228],"modelled":[106,117],"random":[108,123,187],"effects":[109,121,232],"whereas":[110],"baseline":[114,240],"risks":[115],"by":[118],"either":[119],"fixed":[120,231],"or":[122,164],"this":[126],"article,":[127],"we":[128,170],"propose":[129],"metaMOB,":[130],"a":[131,178,186],"procedure":[132],"generalized":[135],"mixed-effects":[136],"model":[137],"tree":[138],"(GLMM":[139],"tree)":[140],"algorithm":[141,196],"subgroup":[143],"IPD":[146],"meta-analysis.":[147],"Although":[148],"application":[150],"metaMOB":[152,181,229],"potentially":[154],"wider,":[155],"e.g.":[156],"experiments":[158,166],"participants":[160],"social":[162],"sciences":[163],"preclinical":[165],"life":[168],"sciences,":[169],"focus":[171],"trials.":[176],"simulation":[179],"study,":[180],"outperformed":[182],"GLMM":[183,201],"trees":[184],"assuming":[185],"intercept":[188],"only":[189],"model-based":[191],"(MOB),":[194],"whose":[195],"basis":[199],"trees,":[202],"respect":[204],"false":[207],"discovery":[208],"rates,":[209],"estimated":[217],"effect.":[219],"most":[221,225],"robust":[222],"therefore":[224],"promising":[226],"method":[227],"modelling":[234],"risks.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2021-08-30T00:00:00"}
