{"doi":"10.1111/sjos.12458","title":"Variable screening for survival data in the presence of heterogeneous censoring","abstract":"<jats:title>Abstract</jats:title><jats:p>Variable screening for censored survival data is most challenging when both survival and censoring times are correlated with an ultrahigh‐dimensional vector of covariates. Existing approaches to handling censoring often make use of inverse probability weighting by assuming independent censoring with both survival time and covariates. This is a convenient but rather restrictive assumption which may be unmet in real applications, especially when the censoring mechanism is complex and the number of covariates is large. To accommodate heterogeneous (covariate‐dependent) censoring that is often present in high‐dimensional survival data, we propose a Gehan‐type rank screening method to select features that are relevant to the survival time. The method is invariant to monotone transformations of the response and of the predictors, and works robustly for a general class of survival models. We establish the sure screening property of the proposed methodology. Simulation studies and a lymphoma data analysis demonstrate its favorable performance and practical utility.</jats:p>","journal":"Scandinavian Journal of Statistics","year":2020,"id":681532,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1780673,"name":"Wai Keung Li","orcid":null,"position":1,"is_corresponding":false},{"id":1177368,"name":"Zhiliang Ying","orcid":null,"position":2,"is_corresponding":false},{"id":688299,"name":"Jinfeng Xu","orcid":"0000-0001-5380-4625","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Variable screening for survival data in the presence of heterogeneous censoring","abstract":"<jats:title>Abstract</jats:title><jats:p>Variable screening for censored survival data is most challenging when both survival and censoring times are correlated with an ultrahigh‐dimensional vector of covariates. Existing approaches to handling censoring often make use of inverse probability weighting by assuming independent censoring with both survival time and covariates. This is a convenient but rather restrictive assumption which may be unmet in real applications, especially when the censoring mechanism is complex and the number of covariates is large. To accommodate heterogeneous (covariate‐dependent) censoring that is often present in high‐dimensional survival data, we propose a Gehan‐type rank screening method to select features that are relevant to the survival time. The method is invariant to monotone transformations of the response and of the predictors, and works robustly for a general class of survival models. We establish the sure screening property of the proposed methodology. Simulation studies and a lymphoma data analysis demonstrate its favorable performance and practical utility.</jats:p>","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26207759","pmcid":null,"openalex_id":"https://openalex.org/W3014640273","authors":[],"funders":[],"total_grants":0,"fwci":0.1998,"citation_percentile":0.51405264,"influential_citations":0,"citation_trend":[{"year":2022,"count":1},{"year":2024,"count":2},{"year":2025,"count":2},{"year":2026,"count":1}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fsjos.12458","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/sjos.12458","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1111/sjos.12458","host_type":"publisher"},{"url":"https://doi.org/10.1111/sjos.12458","host_type":"journal"}],"fields_of_study":["Statistical Methods and Inference","Statistical Methods and Bayesian Inference","Bayesian Methods and Mixture Models"],"mesh_terms":[],"keywords":["Censoring (clinical trials)","Covariate","Inverse probability weighting","Survival analysis","Accelerated failure time model","Statistics","Weighting","Mathematics","Monotone polygon","Econometrics","Computer science","Propensity score matching","Medicine"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-17T18:01:33.049982Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}