{"id":"https://openalex.org/W7202078257","doi":"https://doi.org/10.64898/2026.08.05.26359034","title":"A software package for simple and rigorous survival machine learning analysis in biomedical research","display_name":"A software package for simple and rigorous survival machine learning analysis in biomedical research","publication_year":2026,"publication_date":"2026-08-10","ids":{"openalex":"https://openalex.org/W7202078257","doi":"https://doi.org/10.64898/2026.08.05.26359034","pmid":"https://pubmed.ncbi.nlm.nih.gov/42851270","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13643681"},"language":"en","primary_location":{"id":"doi:10.64898/2026.08.05.26359034","doi":"https://doi.org/10.64898/2026.08.05.26359034","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.08.05.26359034","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/08/10/2026.08.05.26359034.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.medrxiv.org/content/medrxiv/early/2026/08/10/2026.08.05.26359034.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014209770","display_name":"Alyssa F. Pybus","orcid":"https://orcid.org/0000-0002-9722-3977","observed_orcids":["https://orcid.org/0000-0002-9722-3977"]},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alyssa F. Pybus","raw_affiliation_strings":["Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"],"raw_orcid":"https://orcid.org/0000-0002-9722-3977","affiliations":[{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA","institution_ids":["https://openalex.org/I3019308854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132903954","display_name":"Junhao Qiu","orcid":"https://orcid.org/0009-0005-4322-3401","observed_orcids":["https://orcid.org/0009-0005-4322-3401"]},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junhao Qiu","raw_affiliation_strings":["Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"],"raw_orcid":"https://orcid.org/0009-0005-4322-3401","affiliations":[{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA","institution_ids":["https://openalex.org/I3019308854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010091432","display_name":"Paulo Cilas Morais Lyra Junior","orcid":"https://orcid.org/0000-0002-4403-6684","observed_orcids":["https://orcid.org/0000-0002-4403-6684"]},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paulo Cilas Morais Lyra","raw_affiliation_strings":["Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"],"raw_orcid":"https://orcid.org/0000-0002-4403-6684","affiliations":[{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA","institution_ids":["https://openalex.org/I3019308854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123967927","display_name":"Khai Dang","orcid":"https://orcid.org/0009-0008-7774-1380","observed_orcids":["https://orcid.org/0009-0008-7774-1380"]},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Khai Dang","raw_affiliation_strings":["Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"],"raw_orcid":"https://orcid.org/0009-0008-7774-1380","affiliations":[{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA","institution_ids":["https://openalex.org/I3019308854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064792363","display_name":"Isis Yanina Narvaez-Bandera","orcid":"https://orcid.org/0000-0001-7320-618X","observed_orcids":["https://orcid.org/0000-0001-7320-618X"]},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Isis Narvaez-Bandera","raw_affiliation_strings":["Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"],"raw_orcid":"https://orcid.org/0000-0001-7320-618X","affiliations":[{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA","institution_ids":["https://openalex.org/I3019308854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132839461","display_name":"Tosin Jolaogun","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tosin Jolaogun","raw_affiliation_strings":["Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA","institution_ids":["https://openalex.org/I3019308854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031069551","display_name":"Jeremy Goecks","orcid":"https://orcid.org/0000-0002-4583-5226","observed_orcids":["https://orcid.org/0000-0002-4583-5226"]},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jeremy Goecks","raw_affiliation_strings":["Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"],"raw_orcid":"https://orcid.org/0000-0002-4583-5226","affiliations":[{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA","institution_ids":["https://openalex.org/I3019308854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5031069551"],"corresponding_institution_ids":["https://openalex.org/I3019308854"],"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":0.5776000022888184,"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":0.5776000022888184,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.26660001277923584,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.023499999195337296,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/time-to-event-prediction","display_name":"time-to-event prediction","score":0.8560000061988831},{"id":"https://openalex.org/keywords/data-leakage","display_name":"data leakage","score":0.5759999752044678},{"id":"https://openalex.org/keywords/multi-metric-evaluation","display_name":"multi-metric evaluation","score":0.7059999704360962},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.47200000286102295},{"id":"https://openalex.org/keywords/ensemble-methods","display_name":"ensemble methods","score":0.621999979019165},{"id":"https://openalex.org/keywords/kernel-methods","display_name":"kernel methods","score":0.6629999876022339},{"id":"https://openalex.org/keywords/immunotherapy","display_name":"immunotherapy","score":0.4830000102519989},{"id":"https://openalex.org/keywords/overall-survival","display_name":"overall survival","score":0.6159999966621399},{"id":"https://openalex.org/keywords/progression-free-survival","display_name":"progression-free survival","score":0.9940000176429749},{"id":"https://openalex.org/keywords/albumin","display_name":"albumin","score":0.7120000123977661},{"id":"https://openalex.org/keywords/tumor-mutational-burden","display_name":"tumor mutational burden","score":0.9549999833106995},{"id":"https://openalex.org/keywords/high-dimensional-data","display_name":"high-dimensional data","score":0.36800000071525574}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7361999750137329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.676800012588501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6146000027656555},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.517799973487854},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5031999945640564},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.4551999866962433},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33550000190734863},{"id":"https://openalex.org/C2777548347","wikidata":"https://www.wikidata.org/wiki/Q5456937","display_name":"Flagging","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.2680000066757202},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2597000002861023}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.64898/2026.08.05.26359034","doi":"https://doi.org/10.64898/2026.08.05.26359034","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.08.05.26359034","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/08/10/2026.08.05.26359034.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},{"id":"pmh:oai:pubmedcentral.nih.gov:13643681","doi":null,"is_oa":true,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42851270/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13643681/pdf/nihpp-2026.08.05.26359034v1.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"medRxiv","raw_type":"Text"},{"id":"pmid:42851270","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42851270","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"medRxiv : the preprint server for health sciences","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.64898/2026.08.05.26359034","doi":"https://doi.org/10.64898/2026.08.05.26359034","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.08.05.26359034","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/08/10/2026.08.05.26359034.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"sustainable_development_goals":[{"score":0.8999999761581421,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1954557851","display_name":"Developing a Cancer Galaxy Computational Workbench to Meet Emerging Cancer Data Analysis Needs","funder_award_id":"1u24ca284167-01a1","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G9000882172","display_name":"Democratization of Data Analysis in Life Sciences Through Galaxy","funder_award_id":"U24HG006620","funder_id":"https://openalex.org/F4320337348","funder_display_name":"National Human Genome Research Institute"}],"funders":[{"id":"https://openalex.org/F4320311073","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337348","display_name":"National Human Genome Research Institute","ror":"https://ror.org/00baak391"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7202078257.pdf","grobid_xml":"https://content.openalex.org/works/W7202078257.grobid-xml"},"referenced_works_count":84,"referenced_works":["https://openalex.org/W1257569579","https://openalex.org/W1602160603","https://openalex.org/W1966714873","https://openalex.org/W1980283223","https://openalex.org/W1985299015","https://openalex.org/W1986546598","https://openalex.org/W1995945562","https://openalex.org/W2000703258","https://openalex.org/W2005895632","https://openalex.org/W2011284201","https://openalex.org/W2048231652","https://openalex.org/W2052825782","https://openalex.org/W2057722879","https://openalex.org/W2072013249","https://openalex.org/W2082174016","https://openalex.org/W2084139018","https://openalex.org/W2101234009","https://openalex.org/W2101653483","https://openalex.org/W2102636708","https://openalex.org/W2106421416","https://openalex.org/W2110992644","https://openalex.org/W2112316706","https://openalex.org/W2115709314","https://openalex.org/W2119910794","https://openalex.org/W2120579447","https://openalex.org/W2132432664","https://openalex.org/W2136482257","https://openalex.org/W2142457016","https://openalex.org/W2161645441","https://openalex.org/W2236833089","https://openalex.org/W2295598076","https://openalex.org/W2296761362","https://openalex.org/W2618851150","https://openalex.org/W2753919178","https://openalex.org/W2774712455","https://openalex.org/W2783137027","https://openalex.org/W2789172526","https://openalex.org/W2796582438","https://openalex.org/W2898559532","https://openalex.org/W2911964244","https://openalex.org/W2914335731","https://openalex.org/W2943781197","https://openalex.org/W2949676527","https://openalex.org/W2954512644","https://openalex.org/W2963232127","https://openalex.org/W2965512201","https://openalex.org/W2970971581","https://openalex.org/W2977606681","https://openalex.org/W2979333989","https://openalex.org/W2999615587","https://openalex.org/W3035701850","https://openalex.org/W3134715681","https://openalex.org/W3147894994","https://openalex.org/W3154805493","https://openalex.org/W3171472218","https://openalex.org/W3202929943","https://openalex.org/W3210650932","https://openalex.org/W4246259708","https://openalex.org/W4281732693","https://openalex.org/W4288770119","https://openalex.org/W4301159569","https://openalex.org/W4306989117","https://openalex.org/W4324114579","https://openalex.org/W4365446410","https://openalex.org/W4385576721","https://openalex.org/W4391942283","https://openalex.org/W4394857110","https://openalex.org/W4396976722","https://openalex.org/W4399300438","https://openalex.org/W4399695052","https://openalex.org/W4400303161","https://openalex.org/W4401305294","https://openalex.org/W4402552706","https://openalex.org/W4406117717","https://openalex.org/W4406994619","https://openalex.org/W4410812508","https://openalex.org/W4411848483","https://openalex.org/W4415270339","https://openalex.org/W4415295952","https://openalex.org/W4415619070","https://openalex.org/W4415687557","https://openalex.org/W7161629784","https://openalex.org/W7163996244","https://openalex.org/W7164381290"],"related_works":["https://openalex.org/W3032016714","https://openalex.org/W6996966678","https://openalex.org/W3111736660","https://openalex.org/W4404414991","https://openalex.org/W4407185879","https://openalex.org/W4391695414","https://openalex.org/W7102395156","https://openalex.org/W4387349318","https://openalex.org/W4290613737","https://openalex.org/W2518432768"],"abstract_inverted_index":{"Survival":[0,238],"analysis":[1,42,263],"is":[2,97,168],"a":[3,160,219],"fundamental":[4],"technique":[5],"in":[6,20,85],"biomedical":[7,256],"research":[8],"for":[9,51,222],"modeling":[10],"time-to-event":[11],"data.":[12],"It":[13],"enables":[14,255],"the":[15,92,169,174,194,241],"identification":[16],"of":[17,35,213],"prognostic":[18],"factors":[19],"disease,":[21],"compares":[22],"survival":[23,41,62,87,176,203,210,262],"outcomes":[24],"across":[25],"treatment":[26,31],"groups,":[27],"and":[28,76,82,117,123,127,132,135,139,144,155,186,249,264,278],"performs":[29],"targeted":[30],"selection.":[32],"A":[33],"variety":[34],"machine":[36],"learning":[37,157],"(ML)":[38],"approaches":[39],"to":[40,45,172,234,258],"have":[43],"emerged":[44],"complement":[46],"classical":[47],"statistical":[48],"methods,":[49],"especially":[50],"high-dimensional":[52],"datasets":[53],"with":[54,269],"complex,":[55],"nonlinear":[56],"interactions":[57],"between":[58],"features.":[59],"However,":[60],"using":[61,86,266],"ML":[63,88,177],"methods":[64],"requires":[65],"addressing":[66],"challenges":[67],"such":[68],"as":[69],"censoring-unaware":[70],"evaluation,":[71,119,129],"overfitting,":[72],"selecting":[73],"performance":[74],"metrics,":[75],"data":[77],"leakage.":[78],"To":[79,164],"address":[80],"these":[81],"other":[83],"difficulties":[84],"models,":[89],"we":[90],"developed":[91],"mlsurv":[93,96,147,167,192,254],"software":[94,162],"package.":[95,163],"an":[98],"open-source":[99],"Python":[100],"package":[101,171],"built":[102],"around":[103],"three":[104],"major":[105],"design":[106],"principles:":[107],"1)":[108],"methodological":[109,279],"rigor,":[110],"including":[111,125,141],"evidence-based":[112],"model":[113,181],"selection,":[114],"leakage-free":[115],"pipelines,":[116],"multi-metric":[118],"2)":[120],"multi-scale":[121],"evaluation":[122],"interpretation,":[124],"population":[126],"subpopulation":[128],"patient-level":[130],"explanations,":[131],"feature":[133,221],"analysis,":[134],"3)":[136],"automated":[137,180],"trust":[138],"transparency,":[140],"limitation":[142],"flagging":[143],"TRIPOD+AI-aligned":[145],"reporting.":[146],"bundles":[148],"ten":[149],"models":[150,239],"spanning":[151],"linear,":[152],"ensemble,":[153],"kernel,":[154],"deep":[156],"families":[158],"within":[159],"unified":[161],"our":[165],"knowledge,":[166],"first":[170],"span":[173],"complete":[175],"workflow":[178],"from":[179,281],"recommendation":[182],"through":[183],"TRIPOD+AI":[184],"reporting":[185],"individual":[187],"patient":[188],"explanation.":[189],"We":[190,199],"demonstrate":[191],"on":[193,246,252],"Chowell":[195],"immunotherapy":[196],"cohort":[197],"(n=1,479).":[198],"found":[200],"that":[201],"overall":[202],"(OS)":[204],"was":[205,218],"more":[206],"predictable":[207],"than":[208,274],"progression-free":[209],"(PFS)":[211],"(concordance":[212],"0.73":[214],"vs":[215],"0.67).":[216],"Albumin":[217],"top":[220],"both":[223],"endpoints":[224],"but":[225],"dominated":[226],"OS":[227],"prediction,":[228],"whereas":[229],"tumor":[230],"mutational":[231],"burden":[232],"rose":[233],"co-lead":[235],"PFS":[236,247],"prediction.":[237],"matched":[240],"response-trained":[242],"LORIS":[243],"clinical":[244],"score":[245],"prediction":[248],"exceeded":[250],"it":[251],"OS.":[253],"researchers":[257],"conduct":[259],"rigorous,":[260],"multi-model":[261],"benchmarking":[265],"minimal":[267],"code":[268],"default":[270],"best":[271],"practices":[272],"rather":[273],"implementing":[275],"custom":[276],"scripts":[277],"safeguards":[280],"scratch.":[282]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-08-10T00:00:00"}