{"doi":"10.1177/09622802231199335","title":"Variable selection using inverse probability of censoring weighting","abstract":"<jats:p> In this article, we propose two variable selection methods for adjusting the censoring information for survival times, such as the restricted mean survival time. To adjust for the influence of censoring, we consider an inverse probability of censoring weighted for subjects with events. We derive a least absolute shrinkage and selection operator (lasso)-type variable selection method, which considers an inverse weighting for of the squared losses, and an information criterion-type variable selection method, which applies an inverse weighting of the survival probability to the power of each density function in the likelihood function. We prove the consistency of the inverse probability of censoring weighted lasso estimator and the maximum inverse probability of censoring weighted likelihood estimator. The performance of the inverse probability of censoring weighted lasso and inverse probability of censoring weighted information criterion are evaluated via a simulation study with six scenarios, and then their variable selection ability is demonstrated using data from two clinical studies. The results confirm that inverse probability of censoring weighted lasso and the inverse probability of censoring weighted likelihood function produce good estimation accuracy and consistent variable selection. We conclude that our two proposed methods are useful variable selection tools for adjusting the censoring information for survival time analyses. </jats:p>","journal":"Statistical Methods in Medical Research","year":2023,"id":19052,"datarank":0.17570249336458596,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.010910650064369509,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.010910650064369509,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":2,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":2,"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":130293,"name":"Masahiro Kojima","orcid":"0000-0003-0867-7692","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":2,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"37675496","pmcid":null,"openalex_id":"https://openalex.org/W4386505310","authors":[],"funders":[],"total_grants":0,"fwci":0.6315,"citation_percentile":0.71310923,"influential_citations":0,"citation_trend":[{"year":2025,"count":2}],"oa_status":"closed","license":"https://journals.sagepub.com/page/policies/text-and-data-mining-license","oa_locations":[{"url":"https://journals.sagepub.com/doi/pdf/10.1177/09622802231199335","host_type":"publisher"},{"url":"https://journals.sagepub.com/doi/full-xml/10.1177/09622802231199335","host_type":"publisher"},{"url":"https://doi.org/10.1177/09622802231199335","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/37675496","host_type":"repository"}],"fields_of_study":["Statistical Methods and Inference","Statistical Distribution Estimation and Applications","Liver Disease Diagnosis and Treatment","Medicine","Mathematics","Humans","Probability","Survival Analysis","Computer Simulation","Likelihood Functions"],"mesh_terms":["Computer Simulation","Humans","Probability","Likelihood Functions","Survival Analysis"],"keywords":["Censoring (clinical trials)","Inverse probability","Inverse probability weighting","Statistics","Estimator","Mathematics","Weighting","Posterior probability","Bayesian probability","Medicine","Inverse Probability Of Censoring Weighting","Restricted Mean Survival Time"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"},{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[{"doi":"10.25384/sage.24104071","title":"sj-zip-1-smm-10.1177_09622802231199335 - Supplemental material for Variable selection using inverse probability of censoring weighting","publisher":"SAGE Journals","resource_type":"Dataset"},{"doi":"10.25384/sage.24104071.v1","title":"sj-zip-1-smm-10.1177_09622802231199335 - Supplemental material for Variable selection using inverse probability of censoring weighting","publisher":"SAGE Journals","resource_type":"Dataset"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-04T01:33:13.565698Z","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":[]}