{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T17:33:39Z","timestamp":1792431219954,"version":"4.5.0"},"reference-count":84,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004328","name":"Genentech, Inc.","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100004328","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Prognostic models in oncology have a profound impact on personalized cancer care and patient profiling, but tend to be heterogeneously developed and implemented in narrow patient cohorts. Here, we develop and benchmark multiple machine learning models to predict survival in pan-cancer and 16 single-cancer settings using a de-identified clinico-genomic database of 28,079 US patients with cancer. We identify key predictors of cancer prognosis, including 15 shared across seven or more cancer types, revealing strong consistency in cancer prognostic factors. We demonstrate that pan-cancer models generally outperform or match single-cancer models in predicting survival and risk stratifying patients, especially in smaller cancer cohorts, suggesting a unique transfer learning advantage of pan-cancer models. This work demonstrates the potential of pan-cancer approaches in enhancing the accuracy and applicability of prognostic models in oncology, paving the way for more personalized and effective cancer care strategies.<\/jats:p>","DOI":"10.1371\/journal.pone.0341355","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T18:21:53Z","timestamp":1771006913000},"page":"e0341355","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prognostic pan-cancer and single-cancer models: A large-scale analysis using a real-world clinico-genomic database"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2448-6714","authenticated-orcid":true,"given":"Sarah F.","family":"McGough","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana","family":"Lyalina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devin","family":"Incerti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunru","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefka","family":"Tyanova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8131-3118","authenticated-orcid":true,"given":"Kieran","family":"Mace","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Harbron","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Copping","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balasubramanian","family":"Narasimhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Tibshirani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"issue":"5","key":"pone.0341355.ref001","first-page":"370","article-title":"American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine","volume":"66","author":"MW Kattan","year":"2016","journal-title":"CA Cancer J Clin"},{"issue":"6","key":"pone.0341355.ref002","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1164\/rccm.202009-3649ED","article-title":"Risk, results, and costs: optimizing clinical trial efficiency through prognostic enrichment","volume":"203","author":"K Viele","year":"2021","journal-title":"Am J Respir Crit Care Med"},{"issue":"1","key":"pone.0341355.ref003","doi-asserted-by":"crossref","first-page":"33","DOI":"10.2217\/pme.09.49","article-title":"Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology","volume":"7","author":"R Simon","year":"2010","journal-title":"Per Med"},{"issue":"14","key":"pone.0341355.ref004","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1056\/NEJM199309303291402","article-title":"A predictive model for aggressive non-Hodgkin\u2019s lymphoma","volume":"329","author":"International Non-Hodgkin\u2019s Lymphoma Prognostic Factors Project","year":"1993","journal-title":"N Engl J Med"},{"issue":"5","key":"pone.0341355.ref005","article-title":"Simple prognostic model for patients with advanced cancer based on performance status","volume":"10","author":"RW Jang","year":"2014","journal-title":"J Oncol Pract"},{"issue":"1","key":"pone.0341355.ref006","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s12874-022-01577-x","article-title":"Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review","volume":"22","author":"P Dhiman","year":"2022","journal-title":"BMC Med Res Methodol"},{"issue":"10","key":"pone.0341355.ref007","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1016\/S1470-2045(20)30445-9","article-title":"Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study","volume":"21","author":"A Marabelle","year":"2020","journal-title":"Lancet Oncol"},{"issue":"2","key":"pone.0341355.ref008","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S1470-2045(19)30691-6","article-title":"Entrectinib in patients with advanced or metastatic NTRK fusion-positive solid tumours: integrated analysis of three phase 1-2 trials","volume":"21","author":"RC Doebele","year":"2020","journal-title":"Lancet Oncol"},{"issue":"11","key":"pone.0341355.ref009","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1016\/j.annonc.2020.07.013","article-title":"An enhanced prognostic score for overall survival of patients with cancer derived from a large real-world cohort","volume":"31","author":"T Becker","year":"2020","journal-title":"Ann Oncol"},{"issue":"11","key":"pone.0341355.ref010","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1016\/j.annonc.2020.08.2233","article-title":"Pan-cancer prognostic models of clinical outcomes: statistical exercise or clinical tools?","volume":"31","author":"S Halabi","year":"2020","journal-title":"Ann Oncol"},{"issue":"10","key":"pone.0341355.ref011","article-title":"Real-world data prognostic model of overall survival in patients with advanced NSCLC receiving anti-PD-1\/PD-L1 immune checkpoint inhibitors as second-line monotherapy","volume":"5","author":"C Julian","year":"2022","journal-title":"Cancer Rep (Hoboken)"},{"issue":"3","key":"pone.0341355.ref012","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3816\/CLC.2009.n.021","article-title":"Genomic prognostic models in early-stage lung cancer","volume":"10","author":"JR Kratz","year":"2009","journal-title":"Clin Lung Cancer"},{"key":"pone.0341355.ref013","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/1755-8794-4-3","article-title":"Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures","volume":"4","author":"C Fan","year":"2011","journal-title":"BMC Med Genomics"},{"key":"pone.0341355.ref014","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1200\/JCO.2017.35.15_suppl.2514","article-title":"Development and validation of a real-world clinicogenomic database","volume":"35","author":"G Singal","year":"2017","journal-title":"J Clin Oncol"},{"key":"pone.0341355.ref015","unstructured":"Birnbaum B, Nussbaum N, Seidl-Rathkopf K, Agrawal M, Estevez M, Estola E, et al. Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research [Internet]. 2020 [cited 2023 Oct 9]. Available from: http:\/\/arxiv.org\/abs\/2001.09765"},{"issue":"1","key":"pone.0341355.ref016","first-page":"54","article-title":"Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy","volume":"1","author":"B Efron","year":"1986","journal-title":"Stat Sci"},{"key":"pone.0341355.ref017","doi-asserted-by":"crossref","first-page":"112306","DOI":"10.1016\/j.chaos.2022.112306","article-title":"Forecasting new diseases in low-data settings using transfer learning","volume":"161","author":"K Roster","year":"2022","journal-title":"Chaos Solitons Fractals"},{"issue":"6","key":"pone.0341355.ref018","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1097\/00000421-198212000-00014","article-title":"Toxicity and response criteria of the Eastern Cooperative Oncology Group","volume":"5","author":"MM Oken","year":"1982","journal-title":"Am J Clin Oncol"},{"issue":"6","key":"pone.0341355.ref019","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1038\/sj.bjc.6601231","article-title":"Performance status score: do patients and their oncologists agree?","volume":"89","author":"SP Blagden","year":"2003","journal-title":"Br J Cancer"},{"issue":"11","key":"pone.0341355.ref020","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1002\/1097-0142(19860601)57:11<2217::AID-CNCR2820571124>3.0.CO;2-T","article-title":"Age as a prognostic factor in breast cancer","volume":"57","author":"H H\u00f8st","year":"1986","journal-title":"Cancer"},{"issue":"5","key":"pone.0341355.ref021","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.3892\/ol.2013.1566","article-title":"Age is a prognostic factor affecting survival in lung cancer patients","volume":"6","author":"F Tas","year":"2013","journal-title":"Oncol Lett"},{"issue":"3","key":"pone.0341355.ref022","doi-asserted-by":"crossref","first-page":"27","DOI":"10.12927\/hcq.2013.23497","article-title":"The value of collecting population-based cancer stage data to support decision-making at organizational, regional and population levels","volume":"16","author":"JD Brierley","year":"2013","journal-title":"Healthc Q"},{"key":"pone.0341355.ref023","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/1475-2891-9-69","article-title":"Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature","volume":"9","author":"D Gupta","year":"2010","journal-title":"Nutr J"},{"key":"pone.0341355.ref024","article-title":"The prognostic value of pre-treatment hemoglobin (Hb) in patients with advanced or metastatic gastric cancer treated with immunotherapy","volume":"11","author":"M Gou","year":"2021","journal-title":"Front Oncol"},{"issue":"2","key":"pone.0341355.ref025","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S1359-6349(03)00103-4","article-title":"What is the value of hemoglobin as a prognostic and predictive factor in cancer?","volume":"2","year":"2004","journal-title":"Eur J Cancer Suppl"},{"key":"pone.0341355.ref026","article-title":"Prognostic role of pretreatment blood lymphocyte count in patients with solid tumors: a systematic review and meta-analysis","author":"J Zhao","year":"2020","journal-title":"Cancer Cell Int"},{"issue":"12","key":"pone.0341355.ref027","doi-asserted-by":"crossref","first-page":"2214","DOI":"10.1002\/1097-0142(20010615)91:12<2214::AID-CNCR1251>3.0.CO;2-P","article-title":"Anemia as an independent prognostic factor for survival in patients with cancer: a systemic, quantitative review","volume":"91","author":"JJ Caro","year":"2001","journal-title":"Cancer"},{"issue":"2","key":"pone.0341355.ref028","article-title":"A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal","volume":"14","author":"JX Sun","year":"2018","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"pone.0341355.ref029","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms9971","article-title":"Systematic pan-cancer analysis of tumour purity","volume":"6","author":"D Aran","year":"2015","journal-title":"Nat Commun"},{"issue":"15","key":"pone.0341355.ref030","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1038\/sj.onc.1210302","article-title":"TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes","volume":"26","author":"A Petitjean","year":"2007","journal-title":"Oncogene"},{"issue":"24","key":"pone.0341355.ref031","doi-asserted-by":"crossref","first-page":"7345","DOI":"10.1158\/0008-5472.CAN-11-1874","article-title":"The Pax-5 gene: a pluripotent regulator of B-cell differentiation and cancer disease","volume":"71","author":"P O\u2019Brien","year":"2011","journal-title":"Cancer Res"},{"issue":"2","key":"pone.0341355.ref032","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1038\/s41571-018-0115-y","article-title":"Fibroblast growth factor receptors as treatment targets in clinical oncology","volume":"16","author":"M Katoh","year":"2018","journal-title":"Nat Rev Clin Oncol"},{"key":"pone.0341355.ref033","doi-asserted-by":"crossref","first-page":"779918","DOI":"10.3389\/fonc.2021.779918","article-title":"KDM6 demethylases and their roles in human cancers","volume":"11","author":"C Hua","year":"2021","journal-title":"Front Oncol"},{"key":"pone.0341355.ref034","unstructured":"ClinicalTrials.gov [Internet]. [cited 2025 Nov 24]. Available from: https:\/\/clinicaltrials.gov\/study\/NCT03827850"},{"issue":"6","key":"pone.0341355.ref035","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1136\/jcp.2006.039917","article-title":"Pax-5 immunoexpression in various types of benign and malignant tumours: a high-throughput tissue microarray analysis","volume":"60","author":"P Mhawech-Fauceglia","year":"2007","journal-title":"J Clin Pathol"},{"issue":"3","key":"pone.0341355.ref036","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1038\/labinvest.2008.168","article-title":"PAX5 is expressed in small-cell lung cancer and positively regulates c-Met transcription","volume":"89","author":"R Kanteti","year":"2009","journal-title":"Lab Invest"},{"key":"pone.0341355.ref037","article-title":"CN2 ROPRO \u2013 Real-World Data Prognostic Score: A Novel Tool to Assess Patients\u2019 Performance Status","volume":"32","year":"2021","journal-title":"Ann Oncol"},{"issue":"11","key":"pone.0341355.ref038","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1177\/09622802221111549","article-title":"Ensemble methods for survival function estimation with time-varying covariates","volume":"31","author":"W Yao","year":"2022","journal-title":"Stat Methods Med Res"},{"issue":"19","key":"pone.0341355.ref039","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1093\/bioinformatics\/bty357","article-title":"Distinguishing prognostic and predictive biomarkers: an information theoretic approach","volume":"34","author":"K Sechidis","year":"2018","journal-title":"Bioinformatics"},{"issue":"8","key":"pone.0341355.ref040","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1038\/s41591-022-01873-5","article-title":"Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data","volume":"28","author":"R Liu","year":"2022","journal-title":"Nat Med"},{"issue":"9","key":"pone.0341355.ref041","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0237802","article-title":"Clinical and analytical validation of FoundationOne Liquid CDx, a novel 324-Gene cfDNA-based comprehensive genomic profiling assay for cancers of solid tumor origin","volume":"15","author":"R Woodhouse","year":"2020","journal-title":"PLoS One"},{"issue":"11","key":"pone.0341355.ref042","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1038\/nbt.2696","article-title":"Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing","volume":"31","author":"GM Frampton","year":"2013","journal-title":"Nat Biotechnol"},{"issue":"24","key":"pone.0341355.ref043","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.1182\/blood-2015-08-664649","article-title":"Integrated genomic DNA\/RNA profiling of hematologic malignancies in the clinical setting","volume":"127","author":"J He","year":"2016","journal-title":"Blood"},{"issue":"6","key":"pone.0341355.ref044","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing value estimation methods for DNA microarrays","volume":"17","author":"O Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"pone.0341355.ref045","article-title":"TumorFusions: an integrative resource for cancer-associated transcript fusions","volume":"46","author":"X Hu","year":"2018","journal-title":"Nucleic Acids Research"},{"issue":"1","key":"pone.0341355.ref046","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1097\/00005650-199801000-00004","article-title":"Comorbidity measures for use with administrative data","volume":"36","author":"A Elixhauser","year":"1998","journal-title":"Med Care"},{"issue":"6","key":"pone.0341355.ref047","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1097\/MLR.0b013e31819432e5","article-title":"A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data","volume":"47","author":"C van Walraven","year":"2009","journal-title":"Med Care"},{"key":"pone.0341355.ref048","first-page":"855","article-title":"node2vec: Scalable Feature Learning for Networks","volume":"2016","author":"A Grover","year":"2016","journal-title":"KDD"},{"key":"pone.0341355.ref049","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkq897","article-title":"HitPredict: a database of quality assessed protein-protein interactions in nine species","volume":"39","author":"A Patil","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"pone.0341355.ref050","doi-asserted-by":"crossref","DOI":"10.1093\/database\/bav117","article-title":"HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species","volume":"2015","author":"Y L\u00f3pez","year":"2015","journal-title":"Database (Oxford)"},{"issue":"7463","key":"pone.0341355.ref051","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1038\/nature12477","article-title":"Signatures of mutational processes in human cancer","volume":"500","author":"LB Alexandrov","year":"2013","journal-title":"Nature"},{"issue":"1","key":"pone.0341355.ref052","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1186\/s12859-019-3043-7","article-title":"SigsPack, a package for cancer mutational signatures","volume":"20","author":"F Schumann","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"pone.0341355.ref053","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s42256-022-00596-z","article-title":"Learning from data with structured missingness","volume":"5","author":"R Mitra","year":"2023","journal-title":"Nat Mach Intell"},{"issue":"25","key":"pone.0341355.ref054","doi-asserted-by":"crossref","first-page":"5487","DOI":"10.1002\/sim.9136","article-title":"Penalized regression for left-truncated and right-censored survival data","volume":"40","author":"SF McGough","year":"2021","journal-title":"Stat Med"},{"issue":"8","key":"pone.0341355.ref055","first-page":"1315","article-title":"Comments on \u201cChoice of time scale and its effect on significance of predictors in longitudinal studies\u201d by Michael J. Pencina, Martin G. Larson and Ralph B. D\u2019Agostino","volume":"28","author":"MH Gail","year":"2009","journal-title":"Stat Med"},{"issue":"1","key":"pone.0341355.ref056","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization Paths for Generalized Linear Models via Coordinate Descent","volume":"33","author":"J Friedman","year":"2010","journal-title":"J Stat Softw"},{"key":"pone.0341355.ref057","first-page":"1","article-title":"Elastic net regularization paths for all generalized linear models","volume":"106","author":"JK Tay","year":"2023","journal-title":"J Stat Softw"},{"key":"pone.0341355.ref058","unstructured":"R Core Team. R: A language and environment for statistical computing [Internet]. 2021. Available from: https:\/\/www.R-project.org\/"},{"key":"pone.0341355.ref059","volume-title":"dplyr: A Grammar of Data Manipulation","author":"H Wickham","year":"2021"},{"key":"pone.0341355.ref060","volume-title":"dbplyr: A \u201cdplyr\u201d back end for databases","author":"H Wickham","year":"2021"},{"key":"pone.0341355.ref061","unstructured":"Henry L, Wickham H. rlang: Functions for Base Types and Core R and \u201cTidyverse\u201d Features [Internet]. 2023. Available from: https:\/\/CRAN.R-project.org\/package=rlang"},{"key":"pone.0341355.ref062","volume-title":"data.table: Extension of `data.frame`","author":"M Dowle","year":"2021"},{"key":"pone.0341355.ref063","volume-title":"Tidyr: Tidy Messy Data","author":"H Wickham","year":"2021"},{"key":"pone.0341355.ref064","unstructured":"Wickham H, Henry L. purrr: Functional Programming Tools [Internet]. 2023. Available from: https:\/\/CRAN.R-project.org\/package=purrr"},{"key":"pone.0341355.ref065","volume-title":"Wrap R tools for debugging and parametric programming","author":"J Mount","year":"2021"},{"key":"pone.0341355.ref066","volume-title":"Stringr: simple, consistent wrappers for common string operations","author":"H Wickham","year":"2019"},{"key":"pone.0341355.ref067","unstructured":"Russell N. hashmap: The Faster Hash Map [Internet]. 2017. Available from: https:\/\/github.com\/nathan-russell\/hashmap"},{"key":"pone.0341355.ref068","volume-title":"pracma: Practical Numerical Math Functions","author":"HW Borchers","year":"2021"},{"key":"pone.0341355.ref069","unstructured":"Silge J, Chow F, Kuhn M, Wickham H. rsample: General Resampling Infrastructure. 2021."},{"key":"pone.0341355.ref070","unstructured":"Kaplan J. fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables. 2020."},{"key":"pone.0341355.ref071","unstructured":"Bulow E. coder: Deterministic Categorization of Items Based on External Code Data [Internet]. 2023. Available from: https:\/\/docs.ropensci.org\/coder\/"},{"key":"pone.0341355.ref072","first-page":"1","article-title":"mice: Multivariate Imputation by Chained Equations in R [Internet]","volume":"45","author":"S van Buuren","year":"2011","journal-title":"J Stat Softw"},{"key":"pone.0341355.ref073","volume-title":"Impute: Imputation for microarray data","author":"T Hastie","year":"2021"},{"key":"pone.0341355.ref074","unstructured":"Therneau TM. A Package for Survival Analysis in R [Internet]. 2021. Available from: https:\/\/CRAN.R-project.org\/package=survival"},{"key":"pone.0341355.ref075","unstructured":"Kuhn M. caret: Classification and Regression Training [Internet]. 2021. Available from: https:\/\/github.com\/topepo\/caret\/"},{"key":"pone.0341355.ref076","volume-title":"LTRCforests: Ensemble Methods for Survival Data with Time-Varying Covariates","author":"W Yao","year":"2021"},{"issue":"2","key":"pone.0341355.ref077","first-page":"352","article-title":"Survival trees for left-truncated and right-censored data, with application to time-varying covariate data","volume":"18","author":"W Fu","year":"2017","journal-title":"Biostatistics"},{"key":"pone.0341355.ref078","volume-title":"doParallel: Foreach Parallel Adaptor for the \u201cparallel\u201d Package","author":"M Corporation","year":"2020"},{"key":"pone.0341355.ref079","unstructured":"Microsoft WS. foreach: Provides Foreach Looping Construct [Internet]. 2020. Available from: https:\/\/github.com\/RevolutionAnalytics\/foreach"},{"key":"pone.0341355.ref080","unstructured":"Bengtsson H. A Unifying Framework for Parallel and Distributed Processing in R using Futures [Internet]. arXiv [cs.DC]. 2020. Available from: http:\/\/arxiv.org\/abs\/2008.00553"},{"key":"pone.0341355.ref081","unstructured":"Gaujoux R. rngtools: Utility Functions for Working with Random Number Generators [Internet]. 2020. Available from: https:\/\/renozao.github.io\/rngtools"},{"key":"pone.0341355.ref082","unstructured":"Gaujoux R. doRNG: Generic Reproducible Parallel Backend for \u201cforeach\u201d Loops [Internet]. 2020. Available from: https:\/\/renozao.github.io\/doRNG"},{"key":"pone.0341355.ref083","unstructured":"Dar\u00f3czi G. logger: A Lightweight, Modern and Flexible Logging Utility [Internet]. 2021. Available from: https:\/\/daroczig.github.io\/logger\/"},{"key":"pone.0341355.ref084","doi-asserted-by":"crossref","unstructured":"Wickham H. ggplot2: Elegant Graphics for Data Analysis [Internet]. Springer-Verlag New York. 2016. Available from: https:\/\/ggplot2.tidyverse.org","DOI":"10.1007\/978-3-319-24277-4_9"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0341355","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T18:22:04Z","timestamp":1771006924000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0341355"}},"subtitle":[],"editor":[{"given":"Amy McCart","family":"Reed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,2,13]]},"references-count":84,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,13]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0341355","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,13]]}}}