{"id":"https://openalex.org/W7225347998","doi":"https://doi.org/10.21203/rs.3.rs-11151581/v1","title":"Random draws from observed values for missing data: when do they support valid inference? A simulation study with a clinical illustration","display_name":"Random draws from observed values for missing data: when do they support valid inference? A simulation study with a clinical illustration","publication_year":2026,"publication_date":"2026-11-05","ids":{"openalex":"https://openalex.org/W7225347998","doi":"https://doi.org/10.21203/rs.3.rs-11151581/v1"},"language":"en","primary_location":{"id":"doi:10.21203/rs.3.rs-11151581/v1","doi":"https://doi.org/10.21203/rs.3.rs-11151581/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11151581/v1","pdf_url":"https://www.researchsquare.com/article/rs-11151581/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","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://www.researchsquare.com/article/rs-11151581/latest.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5159198509","display_name":"Nyiko Muhluri Khoza\u00b9","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I165390105","display_name":"University of South Africa","ror":"https://ror.org/048cwvf49","country_code":"ZA","type":"education","lineage":["https://openalex.org/I165390105"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Nyiko Muhluri Khoza\u00b9","raw_affiliation_strings":["University of South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Africa","institution_ids":["https://openalex.org/I165390105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073968263","display_name":"Bhekisipho Twala","orcid":"https://orcid.org/0000-0002-3452-9581","observed_orcids":["https://orcid.org/0000-0002-3452-9581"]},"institutions":[{"id":"https://openalex.org/I160825796","display_name":"Mangosuthu University of Technology","ror":"https://ror.org/054r97095","country_code":"ZA","type":"education","lineage":["https://openalex.org/I160825796"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Bhekisipho Twala","raw_affiliation_strings":["Mangosuthu University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mangosuthu University of Technology","institution_ids":["https://openalex.org/I160825796"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9990000128746033,"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":0.9990000128746033,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":9.999999747378752e-05,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":9.999999747378752e-05,"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/missing-data-imputation","display_name":"missing data imputation","score":0.8109999895095825},{"id":"https://openalex.org/keywords/predictive-mean-matching","display_name":"predictive mean matching","score":0.6359999775886536},{"id":"https://openalex.org/keywords/missing-completely-at-random","display_name":"missing completely at random","score":0.5059999823570251},{"id":"https://openalex.org/keywords/mar","display_name":"MAR","score":0.9980000257492065},{"id":"https://openalex.org/keywords/missing-not-at-random","display_name":"missing not at random","score":0.9950000047683716},{"id":"https://openalex.org/keywords/imputation-uncertainty","display_name":"imputation uncertainty","score":0.36000001430511475},{"id":"https://openalex.org/keywords/confidence-interval-coverage","display_name":"confidence interval coverage","score":0.6890000104904175},{"id":"https://openalex.org/keywords/rubin-s-rules","display_name":"Rubin's rules","score":0.7450000047683716},{"id":"https://openalex.org/keywords/class-variables","display_name":"class variables","score":0.40799999237060547},{"id":"https://openalex.org/keywords/maximum-heart-rate","display_name":"maximal heart rate","score":0.6639999747276306},{"id":"https://openalex.org/keywords/age","display_name":"age","score":0.4950000047683716}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7957000136375427},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7806000113487244},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6266999840736389},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5637000203132629},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.41519999504089355},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C18747219","wikidata":"https://www.wikidata.org/wiki/Q620994","display_name":"Standard error","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.29670000076293945},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.28600001335144043},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C102094743","wikidata":"https://www.wikidata.org/wiki/Q133871","display_name":"Normal distribution","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21203/rs.3.rs-11151581/v1","doi":"https://doi.org/10.21203/rs.3.rs-11151581/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11151581/v1","pdf_url":"https://www.researchsquare.com/article/rs-11151581/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.21203/rs.3.rs-11151581/v1","doi":"https://doi.org/10.21203/rs.3.rs-11151581/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11151581/v1","pdf_url":"https://www.researchsquare.com/article/rs-11151581/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225347998.pdf","grobid_xml":"https://content.openalex.org/works/W7225347998.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1965268674","https://openalex.org/W1972851018","https://openalex.org/W2020960798","https://openalex.org/W2064186732","https://openalex.org/W2096863518","https://openalex.org/W2100358124","https://openalex.org/W2116814040","https://openalex.org/W2118502261","https://openalex.org/W2134843796","https://openalex.org/W2156267802","https://openalex.org/W2162313689","https://openalex.org/W2775363893","https://openalex.org/W2891381594","https://openalex.org/W2955443275","https://openalex.org/W4200310129"],"related_works":["https://openalex.org/W4387098410","https://openalex.org/W7136637946","https://openalex.org/W2130892589","https://openalex.org/W4386754034","https://openalex.org/W7100837165","https://openalex.org/W7214672010","https://openalex.org/W3197601675","https://openalex.org/W7125676204","https://openalex.org/W251386495","https://openalex.org/W7163893997"],"abstract_inverted_index":{"Abstract":[0],"Imputation":[1,48,79],"methods":[2],"that":[3,335],"draw":[4],"replacement":[5],"values":[6,69],"from":[7,62],"a":[8,90,121],"variable's":[9],"admissible":[10],"domain":[11],"are":[12,16,346],"attractive":[13],"because":[14],"they":[15],"simple":[17],"and":[18,57,75,93,104,133,137,141,162,207,228,261,293,306,312,339,342],"never":[19,202],"produce":[20],"structurally":[21],"invalid":[22,205],"values.":[23],"This":[24],"study":[25,124,265],"asks":[26],"when":[27,182,231,328,343],"such":[28],"draws":[29,51,61,85,330],"support":[30],"valid":[31,325],"statistical":[32],"inference.":[33],"The":[34],"Random-Draw":[35,46,76],"Observation":[36,47,78],"Framework":[37],"(RDOF)":[38],"is":[39,316],"examined":[40],"in":[41,171,178,298],"three":[42,217],"forms:":[43],"the":[44,54,63,67,71,83,94,107,112,115,155,168,187,191,232,283,295,329,333,337,340,344],"original":[45],"(ROI),":[49],"which":[50,60,81,242],"uniformly":[52],"between":[53,310],"observed":[55,68,245],"minimum":[56],"maximum;":[58],"ROI-E,":[59],"empirical":[64],"distribution":[65],"of":[66,110,125,176,266,285,302],"via":[70],"approximate":[72],"Bayesian":[73],"bootstrap;":[74],"Class":[77],"(RCOI-E),":[80],"applies":[82],"same":[84],"within":[86],"cells":[87],"defined":[88],"by":[89,158,194,291],"class":[91,143,233],"variable":[92,234],"analysis":[95,338],"variables.":[96],"We":[97],"combine":[98],"completed":[99],"datasets":[100],"using":[101],"Rubin's":[102],"rules":[103],"also":[105,243],"assess":[106],"common":[108],"practice":[109],"retaining":[111],"imputation":[113,189,323],"with":[114],"smallest":[116],"standard":[117],"error":[118],"(SE).":[119],"In":[120],"pre-specified":[122],"simulation":[123],"72":[126],"scenarios":[127],"(six":[128],"marginal":[129],"distributions;":[130],"MCAR,":[131],"MAR":[132,226],"MNAR":[134],"mechanisms;":[135],"30%":[136,213],"60%":[138,225,278],"missingness;":[139],"strong":[140],"weak":[142],"variables;":[144],"n":[145],"=":[146],"340;":[147],"500":[148],"replications":[149,177],"per":[150],"scenario),":[151],"uniform-range":[152,280],"ROI":[153,281],"attenuated":[154,282],"regression":[156],"slope":[157,170,210,284,297],"55\u201383%":[159],"on":[160,287,332],"average,":[161],"its":[163],"95%":[164],"confidence":[165],"intervals":[166],"covered":[167,294],"true":[169,296],"no":[172],"more":[173,248],"than":[174,349],"16%":[175],"any":[179],"scenario":[180],"(6%":[181],"averaged":[183],"over":[184],"distributions).":[185],"Selecting":[186],"minimum-SE":[188],"understated":[190],"mean":[192,239],"SE":[193],"up":[195],"to":[196,222,229],"68%":[197],"without":[198],"reducing":[199],"bias.":[200],"RCOI-E":[201,253,305],"produced":[203],"an":[204],"value":[206],"achieved":[208],"93\u201396%":[209],"coverage":[211,220,309],"at":[212,224,277,299],"missingness":[214,227],"under":[215,250,259],"all":[216],"mechanisms.":[218],"Still,":[219],"fell":[221],"85%":[223],"58%":[230],"was":[235,247,254],"weakly":[236],"informative.":[237],"Predictive":[238],"matching":[240],"(PMM),":[241],"imputes":[244],"values,":[246],"robust":[249],"MAR,":[251],"whereas":[252,304],"comparable":[255],"or":[256],"slightly":[257],"better":[258],"MCAR":[260],"MNAR.":[262],"A":[263],"resampling":[264],"270":[267],"complete":[268],"clinical":[269],"records":[270],"(Statlog":[271],"Heart":[272],"data)":[273],"reproduced":[274],"these":[275],"patterns:":[276],"missingness,":[279,341],"age":[286],"maximum":[288],"heart":[289],"rate":[290],"71\u201374%":[292],"most":[300],"1%":[301],"replications,":[303],"PMM":[307],"kept":[308],"91%":[311],"95%.":[313],"Value-format":[314],"preservation":[315],"therefore":[317],"necessary":[318],"but":[319],"not":[320],"sufficient:":[321],"random-draw":[322],"supports":[324],"inference":[326],"only":[327],"condition":[331],"variables":[334],"drive":[336],"imputations":[345],"pooled":[347],"rather":[348],"selected.":[350]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-06T00:00:00"}