{"id":"https://openalex.org/W7223088901","doi":"https://doi.org/10.48550/arxiv.2610.28379","title":"Interpretable subgroup identification across survival and generalized-linear outcomes","display_name":"Interpretable subgroup identification across survival and generalized-linear outcomes","publication_year":2026,"publication_date":"2026-10-20","ids":{"openalex":"https://openalex.org/W7223088901","doi":"https://doi.org/10.48550/arxiv.2610.28379"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.28379","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.28379","pdf_url":"https://arxiv.org/pdf/2610.28379","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.28379","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088513931","display_name":"Larry F. Le\u00f3n","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leon, Larry F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5078528528","display_name":"Keaven M. Anderson","orcid":"https://orcid.org/0000-0003-3218-1363","observed_orcids":["https://orcid.org/0000-0003-3218-1363"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anderson, Keaven M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10136","display_name":"Statistical Methods and Inference","score":0.6410999894142151,"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":0.6410999894142151,"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":0.2809999883174896,"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":0.06350000202655792,"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/subgroup-identification","display_name":"subgroup identification","score":0.7879999876022339},{"id":"https://openalex.org/keywords/causal-forest","display_name":"causal forest","score":0.5509999990463257},{"id":"https://openalex.org/keywords/stability-selection","display_name":"stability selection","score":0.6539999842643738},{"id":"https://openalex.org/keywords/selection-bias-correction","display_name":"selection bias correction","score":0.5260000228881836},{"id":"https://openalex.org/keywords/survival-outcomes","display_name":"survival outcomes","score":0.5270000100135803},{"id":"https://openalex.org/keywords/generalized-linear-models","display_name":"generalized linear models","score":0.7450000047683716},{"id":"https://openalex.org/keywords/clinical-trials","display_name":"clinical trials","score":0.699999988079071}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6675000190734863},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6456999778747559},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6394000053405762},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5618000030517578},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5386999845504761},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5318999886512756},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4993000030517578},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.476500004529953},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.38420000672340393},{"id":"https://openalex.org/C183905921","wikidata":"https://www.wikidata.org/wiki/Q1038757","display_name":"Multiple comparisons problem","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3783000111579895},{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.3644999861717224},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3346000015735626},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C70519679","wikidata":"https://www.wikidata.org/wiki/Q6666755","display_name":"Log-linear model","level":3,"score":0.2915000021457672},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2694000005722046},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2549999952316284},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.28379","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.28379","pdf_url":"https://arxiv.org/pdf/2610.28379","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"doi:10.48550/arxiv.2610.28379","doi":"https://doi.org/10.48550/arxiv.2610.28379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.28379","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.28379","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.28379","pdf_url":"https://arxiv.org/pdf/2610.28379","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"sustainable_development_goals":[{"score":0.46810001134872437,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223088901.pdf","grobid_xml":"https://content.openalex.org/works/W7223088901.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W7215098495"],"related_works":["https://openalex.org/W7215098495","https://openalex.org/W2800844746","https://openalex.org/W4400262692","https://openalex.org/W2404967015","https://openalex.org/W7083296497","https://openalex.org/W3133705287","https://openalex.org/W1982637896","https://openalex.org/W3036284897","https://openalex.org/W3010019900","https://openalex.org/W4386911015"],"abstract_inverted_index":{"A":[0,52],"subgroup":[1,15],"analysis":[2],"searching":[3],"a":[4,14,28,32,80,84,94,146,162,179],"trial's":[5],"covariates":[6],"or":[7,83],"many":[8],"prespecified":[9],"subgroups":[10],"often":[11],"singles":[12],"out":[13],"for":[16,27,67],"its":[17],"extremity,":[18],"interpreted":[19],"as":[20],"if":[21],"prespecified.":[22],"Selection":[23],"inflates":[24],"the":[25,35,41,59,108,116,126,129,132,135,183],"estimate;":[26],"given":[29],"selection":[30,121],"rule,":[31],"correction":[33],"removes":[34],"inflation.":[36],"Two":[37,185],"questions":[38],"belong":[39],"to":[40,45,49,58],"rule:":[42],"which":[43,111],"candidate":[44],"return,":[46],"and":[47,125,158,176,188,196],"whether":[48,55],"declare":[50],"none.":[51],"third":[53],"is":[54,118,143],"inference":[56],"extends":[57],"patients":[60],"left":[61],"outside.":[62],"We":[63],"treat":[64],"all":[65],"three":[66],"interpretable":[68],"subgroups,":[69],"defined":[70],"by":[71,77],"conjunctions":[72],"of":[73,91,128],"covariate":[74],"cuts,":[75],"generated":[76],"forest":[78,82],"search,":[79],"causal":[81],"cross-fit":[85],"surface":[86],"estimator":[87],"under":[88,169,173],"one":[89],"set":[90],"rules,":[92],"each":[93],"modelling":[95],"choice":[96],"trading":[97],"size":[98],"against":[99],"effect.":[100],"Forest":[101],"search":[102],"screens":[103],"candidates":[104],"on":[105,178],"splitting":[106],"consistency,":[107],"rate":[109,138],"at":[110,144],"effects":[112],"replicate":[113],"across":[114],"half-splits;":[115],"screen":[117],"complementary-pairs":[119],"stability":[120],"in":[122,161],"closed":[123],"form,":[124],"law":[127],"maximum":[130],"over":[131],"family":[133,180],"gives":[134,153],"family-wise":[136],"false-declaration":[137],"when":[139],"every":[140],"candidate's":[141],"effect":[142],"least":[145],"clinically":[147],"specified":[148],"benefit.":[149],"Refit-free":[150],"multiplier":[151],"resampling":[152],"adjusted":[154],"one-sided":[155],"bounds":[156],"(subgroup":[157],"complement),":[159],"joined":[160],"two-subgroup":[163],"claim,":[164],"with":[165],"coverage":[166],"asymptotically":[167],"exact":[168],"separated":[170],"selection,":[171],"characterized":[172],"local":[174],"competition,":[175],"conditional":[177],"proposed":[181],"from":[182,191],"outcomes.":[184],"trial":[186],"applications,":[187],"simulations":[189],"built":[190],"them,":[192],"cover":[193],"survival,":[194],"binary":[195],"continuous":[197],"endpoints.":[198]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-22T00:00:00"}