{"id":"https://openalex.org/W7225313342","doi":"https://doi.org/10.31234/osf.io/6b437_v1","title":"When One Random Effect Is Not Enough: Mixture Model Meta-Analysis","display_name":"When One Random Effect Is Not Enough: Mixture Model Meta-Analysis","publication_year":2026,"publication_date":"2026-11-05","ids":{"openalex":"https://openalex.org/W7225313342","doi":"https://doi.org/10.31234/osf.io/6b437_v1"},"language":"en","primary_location":{"id":"doi:10.31234/osf.io/6b437_v1","doi":"https://doi.org/10.31234/osf.io/6b437_v1","is_oa":true,"landing_page_url":"https://doi.org/10.31234/osf.io/6b437_v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401687","display_name":"PsyArXiv (OSF Preprints)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2799848540","host_organization_name":"Center for Open Science","host_organization_lineage":["https://openalex.org/I2799848540"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.31234/osf.io/6b437_v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069426228","display_name":"Caspar J. Van Lissa","orcid":"https://orcid.org/0000-0002-0808-5024","observed_orcids":["https://orcid.org/0000-0002-0808-5024"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Caspar J. Van Lissa","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-0808-5024","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5069426228"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10206","display_name":"Meta-analysis and systematic reviews","score":0.9800000190734863,"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"}},"topics":[{"id":"https://openalex.org/T10206","display_name":"Meta-analysis and systematic reviews","score":0.9800000190734863,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.005200000014156103,"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":0.005200000014156103,"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/latent-class-analysis","display_name":"latent class analysis","score":0.9359999895095825},{"id":"https://openalex.org/keywords/study-heterogeneity","display_name":"study heterogeneity","score":0.9150000214576721},{"id":"https://openalex.org/keywords/publication-bias","display_name":"publication bias","score":0.6669999957084656},{"id":"https://openalex.org/keywords/outliers","display_name":"outliers","score":0.9210000038146973},{"id":"https://openalex.org/keywords/information-criteria","display_name":"information criteria","score":0.7749999761581421},{"id":"https://openalex.org/keywords/forecasting-model-comparison","display_name":"forecasting model comparison","score":0.800000011920929},{"id":"https://openalex.org/keywords/openmx","display_name":"OpenMx","score":0.8180000185966492},{"id":"https://openalex.org/keywords/structural-equation-modeling","display_name":"structural equation modeling","score":0.8429999947547913},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.32199999690055847}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7142000198364258},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6419000029563904},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5947999954223633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5275999903678894},{"id":"https://openalex.org/C156340839","wikidata":"https://www.wikidata.org/wiki/Q2704791","display_name":"Enumeration","level":2,"score":0.5015000104904175},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.439300000667572},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41119998693466187},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.40700000524520874},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3538999855518341},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C93225998","wikidata":"https://www.wikidata.org/wiki/Q1941972","display_name":"Moderation","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C2988343187","wikidata":"https://www.wikidata.org/wiki/Q1130645","display_name":"Open source software","level":3,"score":0.29420000314712524},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28859999775886536},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2689000070095062},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.31234/osf.io/6b437_v1","doi":"https://doi.org/10.31234/osf.io/6b437_v1","is_oa":true,"landing_page_url":"https://doi.org/10.31234/osf.io/6b437_v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401687","display_name":"PsyArXiv (OSF Preprints)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2799848540","host_organization_name":"Center for Open Science","host_organization_lineage":["https://openalex.org/I2799848540"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.31234/osf.io/6b437_v1","doi":"https://doi.org/10.31234/osf.io/6b437_v1","is_oa":true,"landing_page_url":"https://doi.org/10.31234/osf.io/6b437_v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401687","display_name":"PsyArXiv (OSF Preprints)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2799848540","host_organization_name":"Center for Open Science","host_organization_lineage":["https://openalex.org/I2799848540"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7165667521","https://openalex.org/W7112262342","https://openalex.org/W1546892667","https://openalex.org/W6925001216","https://openalex.org/W3094169133","https://openalex.org/W7223203603","https://openalex.org/W7207046919","https://openalex.org/W2982160553","https://openalex.org/W7131828210","https://openalex.org/W4297861389"],"abstract_inverted_index":{"Handling":[0],"between-study":[1],"differences":[2],"in":[3,12,116,220],"effect":[4,49,205],"sizes,":[5],"or":[6,30,112],"heterogeneity,":[7,29],"is":[8,19,94,171,181,200,217],"a":[9,131,209,214],"key":[10],"challenge":[11],"meta-analysis.":[13,221],"The":[14],"common":[15],"assumption":[16],"that":[17,37,74,168],"heterogeneity":[18,36],"normally":[20],"distributed":[21],"implies":[22],"an":[23],"absence":[24],"of":[25,28,35,148,195,211],"systematic":[26],"sources":[27,34],"known":[31,80,193],"and":[32,68,126,141,145,156,164,173,185],"measured":[33],"can":[38,87,122],"be":[39,123],"controlled":[40],"using":[41],"moderator":[42],"analysis.":[43],"These":[44],"assumptions":[45],"fall":[46],"short":[47],"when":[48,204],"sizes":[50,206],"originate":[51,207],"from":[52,208],"multiple":[53],"unknown":[54],"subpopulations":[55],"-":[56,78,86,190],"for":[57,99,108],"example,":[58],"due":[59],"to":[60,134],"different":[61],"research":[62],"traditions,":[63],"unmeasured":[64],"moderators,":[65],"publication":[66],"bias,":[67,176],"outliers.":[69],"It":[70],"has":[71,174],"been":[72],"argued":[73],"finite":[75],"mixture":[76,210],"models":[77,121],"also":[79],"as":[81],"latent":[82],"class":[83,150,186],"analysis":[84],"(LCA)":[85],"capture":[88],"these":[89,118],"unobserved":[90,212],"subgroups,":[91],"but":[92,177],"there":[93],"currently":[95],"no":[96],"generalized":[97],"framework":[98],"estimating":[100,109],"such":[101],"models.":[102],"We":[103,129],"introduce":[104],"open":[105],"source":[106],"software":[107],"random-effects":[110],"mixture,":[111],"\"remix\"":[113],"meta-analyses.":[114],"Implemented":[115],"`OpenMx`,":[117],"structural":[119],"equation":[120],"fully":[124],"customized":[125],"further":[127],"extended.":[128],"conducted":[130],"simulation":[132],"study":[133],"evaluate":[135],"the":[136,146,153,169,201],"`remix`":[137],"algorithm's":[138],"convergence,":[139],"bias":[140],"confidence":[142,178],"interval":[143,179],"coverage,":[144],"performance":[147],"several":[149],"enumeration":[151,187],"metrics:":[152],"AIC,":[154],"BIC,":[155],"ICL":[157],"information":[158],"criteria,":[159],"predictive":[160],"model":[161],"comparison":[162],"(PMC),":[163],"cross-validation.":[165],"Results":[166],"show":[167],"method":[170,203],"fast":[172],"low":[175],"coverage":[180],"below":[182],"nominal":[183],"levels,":[184],"remains":[188],"challenging":[189],"which":[191,216],"are":[192],"limitations":[194],"LCA.":[196],"Nevertheless,":[197],"remix":[198],"meta-analysis":[199],"appropriate":[202],"subpopulations;":[213],"scenario":[215],"arguably":[218],"commonplace":[219]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-06T00:00:00"}