{"doi":"10.1002/sim.8888","title":"Displaying survival of patient groups defined by covariate paths: Extensions of the Kaplan‐Meier estimator","abstract":"<jats:p>Extensions of the Kaplan‐Meier estimator have been developed to illustrate the relationship between a time‐varying covariate of interest and survival. In particular, Snapinn et al and Xu et al developed estimators to display survival for patients who always have a certain value of a time‐varying covariate. These estimators properly handle time‐varying covariates, but their clinical interpretation is limited. It is of greater clinical interest to display survival for patients whose covariates lie along certain defined paths. In this article, we propose extensions of Snapinn et al and Xu et al's estimators, providing crude and covariate‐adjusted estimates of the survival function for patients defined by covariate paths. We also derive analytical variance estimators. We demonstrate the utility of these estimators with medical examples and a simulation study.</jats:p>","journal":"Statistics in Medicine","year":2021,"id":17674,"datarank":0.6225983215460218,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.26291403062626617,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.26291403062626617,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":10,"citers_with_citation_signal":5,"citers_with_endowment":5,"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":125643,"name":"Rebecca A. Betensky","orcid":"0000-0002-3793-1437","position":1,"is_corresponding":false},{"id":125642,"name":"Melissa Jay","orcid":"0000-0002-8740-4803","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"33530128","pmcid":"PMC8312265","openalex_id":"https://openalex.org/W3127763960","authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"R01NS094610","title":null},{"funder_name":"National Institutes of Health","grant_id":"T32CA009337","title":null},{"funder_name":"National Science Foundation","grant_id":"NSF GRFP Grant No. 000390183","title":null},{"funder_name":"NCI NIH HHS","grant_id":"R01 CA075971","title":null},{"funder_name":"NIA NIH HHS","grant_id":"P30 AG066512","title":null},{"funder_name":"National Institutes of Health","grant_id":"5T32CA009337-20","title":"BIOMETRY/EPIDEMIOLOGY TRAINING GRANT IN BIOSTATISTICS"},{"funder_name":"National Institutes of Health","grant_id":"5R01NS094610-02","title":"Statistical methods for censored and dependently truncated data"}],"total_grants":7,"fwci":1.4025,"citation_percentile":0.82245292,"influential_citations":0,"citation_trend":[{"year":2021,"count":1},{"year":2022,"count":2},{"year":2023,"count":2},{"year":2024,"count":1},{"year":2025,"count":3},{"year":2026,"count":1}],"oa_status":"green","license":"Wiley Online Library User Agreement","oa_locations":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8312265","host_type":"repository"},{"url":"https://rss.onlinelibrary.wiley.com/doi/am-pdf/10.1002/sim.8888","host_type":"BRONZE"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8312265","host_type":"repository"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8888","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1002/sim.8888","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/am-pdf/10.1002/sim.8888","host_type":"publisher"},{"url":"https://doi.org/10.1002/sim.8888","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/33530128","host_type":"repository"},{"url":"https://zbmath.org/7934368","host_type":""},{"url":"https://dx.doi.org/10.1002/sim.8888","host_type":""}],"fields_of_study":["Statistical Methods and Inference","Advanced Causal Inference Techniques","Statistical Methods and Bayesian Inference","Medicine","03 medical and health sciences","0302 clinical medicine","0101 mathematics","01 natural sciences","Computer Simulation","Humans","Survival Analysis"],"mesh_terms":["Computer Simulation","Humans","Survival Analysis"],"keywords":["Covariate","Estimator","Survival analysis","Statistics","Kaplan–Meier estimator","Survival function","Econometrics","Mathematics","Proportional hazards model","Time-dependent Covariates","Time-varying Covariates","Survival Distribution","Humans","Computer Simulation","Applications of statistics to biology and medical sciences; meta analysis"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-02T19:47:47.993511Z","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":[]}