{"doi":"10.1177/09622802231226328","title":"Comparison between inverse-probability weighting and multiple imputation in Cox model with missing failure subtype","abstract":"<jats:p> Identifying and distinguishing risk factors for heterogeneous disease subtypes has been of great interest. However, missingness in disease subtypes is a common problem in those data analyses. Several methods have been proposed to deal with the missing data, including complete-case analysis, inverse-probability weighting, and multiple imputation. Although extant literature has compared these methods in missing problems, none has focused on the competing risk setting. In this paper, we discuss the assumptions required when complete-case analysis, inverse-probability weighting, and multiple imputation are used to deal with the missing failure subtype problem, focusing on how to implement these methods under various realistic scenarios in competing risk settings. Besides, we compare these three methods regarding their biases, efficiency, and robustness to model misspecifications using simulation studies. Our results show that complete-case analysis can be seriously biased when the missing completely at random assumption does not hold. Inverse-probability weighting and multiple imputation estimators are valid when we correctly specify the corresponding models for missingness and for imputation, and multiple imputation typically shows higher efficiency than inverse-probability weighting. However, in real-world studies, building imputation models for the missing subtypes can be more challenging than building missingness models. In that case, inverse-probability weighting could be preferred for its easy usage. We also propose two automated model selection procedures and demonstrate their usage in a study of the association between smoking and colorectal cancer subtypes in the Nurses’ Health Study and Health Professional Follow-Up Study. </jats:p>","journal":"Statistical Methods in Medical Research","year":2024,"id":19446,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":3,"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":131777,"name":"Benjamin Langworthy","orcid":"0000-0001-6735-3853","position":1,"is_corresponding":false},{"id":3732,"name":"Shuji Ogino","orcid":"0000-0002-3909-2323","position":2,"is_corresponding":false},{"id":131778,"name":"Molin Wang","orcid":"0000-0003-1951-8961","position":3,"is_corresponding":false},{"id":131776,"name":"Fuyu Guo","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38262434","pmcid":null,"openalex_id":"https://openalex.org/W4391126608","authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"P01 CA87969","title":null},{"funder_name":"National Institutes of Health","grant_id":"R35 CA 197735","title":null},{"funder_name":"National Institutes of Health","grant_id":"U01 CA167552","title":null},{"funder_name":"National Institutes of Health","grant_id":"UM1 CA186107","title":null},{"funder_name":"Dana-Farber Cancer Institute","grant_id":"Nodal award","title":null},{"funder_name":"NCI NIH HHS","grant_id":"P01 CA087969","title":null}],"total_grants":6,"fwci":1.873,"citation_percentile":0.83725394,"influential_citations":0,"citation_trend":[{"year":2025,"count":2},{"year":2026,"count":1}],"oa_status":"closed","license":"https://journals.sagepub.com/page/policies/text-and-data-mining-license","oa_locations":[{"url":"https://journals.sagepub.com/doi/pdf/10.1177/09622802231226328","host_type":"publisher"},{"url":"https://journals.sagepub.com/doi/full-xml/10.1177/09622802231226328","host_type":"publisher"},{"url":"https://doi.org/10.1177/09622802231226328","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38262434","host_type":"repository"}],"fields_of_study":["Advanced Causal Inference Techniques","Statistical Methods and Inference","Statistical Methods and Bayesian Inference","Medicine","Mathematics","Humans","Proportional Hazards Models","Models, Statistical","Follow-Up Studies","Data Interpretation, Statistical","Probability","Computer Simulation","Risk Factors"],"mesh_terms":["Computer Simulation","Data Interpretation, Statistical","Follow-Up Studies","Humans","Probability","Risk Factors","Models, Statistical","Proportional Hazards Models"],"keywords":["Inverse probability weighting","Missing data","Imputation (statistics)","Weighting","Inverse probability","Computer science","Estimator","Statistics","Data mining","Robustness (evolution)","Econometrics","Mathematics","Posterior probability","Artificial intelligence","Machine learning","Medicine","Bayesian probability","Multiple Imputation","Competing Risk","Complete-case Analysis","Inverse-probability Weighting","Missing Disease Subtype"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-04T04:07:14.912912Z","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":[]}