{"doi":"10.1177/0962280211403597","title":"On weighting approaches for missing data","abstract":"<jats:p>We review the class of inverse probability weighting (IPW) approaches for the analysis of missing data under various missing data patterns and mechanisms. The IPW methods rely on the intuitive idea of creating a pseudo-population of weighted copies of the complete cases to remove selection bias introduced by the missing data. However, different weighting approaches are required depending on the missing data pattern and mechanism. We begin with a uniform missing data pattern (i.e. a scalar missing indicator indicating whether or not the full data is observed) to motivate the approach. We then generalise to more complex settings. Our goal is to provide a conceptual overview of existing IPW approaches and illustrate the connections and differences among these approaches.</jats:p>","journal":"Statistical Methods in Medical Research","year":2013,"id":18978,"datarank":5.75860081263735,"base_score":4.709530201312334,"endowment":4.709530201312334,"self_citation_contribution":0.7064295301968502,"citation_network_contribution":5.0521712824405,"self_endowment_contribution":0.7064295301968502,"citer_contribution":5.0521712824405,"corpus_percentile":null,"corpus_rank":null,"citation_count":110,"citer_count":106,"citers_with_citation_signal":87,"citers_with_endowment":87,"datacite_reuse_total":2,"is_dataset":false,"is_dataset_confidence":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":129966,"name":"Changyu Shen","orcid":null,"position":1,"is_corresponding":false},{"id":129967,"name":"Xiaochun Li","orcid":null,"position":2,"is_corresponding":false},{"id":129968,"name":"James M Robins","orcid":null,"position":3,"is_corresponding":false},{"id":129965,"name":"Lingling Li","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":4.709530201312334,"endowment":4.709530201312334,"datacite_reuse_total":2,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21705435","pmcid":"PMC3998729","openalex_id":"https://openalex.org/W2027846336","authors":[],"funders":[{"funder_name":"NIAID NIH HHS","grant_id":"R37 AI032475","title":null}],"total_grants":1,"fwci":3.3493,"citation_percentile":0.92731307,"influential_citations":1,"citation_trend":[{"year":2012,"count":1},{"year":2013,"count":4},{"year":2014,"count":7},{"year":2015,"count":7},{"year":2016,"count":11},{"year":2017,"count":8},{"year":2018,"count":5},{"year":2019,"count":8},{"year":2020,"count":7},{"year":2021,"count":8},{"year":2022,"count":10},{"year":2023,"count":14},{"year":2024,"count":9},{"year":2025,"count":7},{"year":2026,"count":4}],"oa_status":"closed","license":"https://journals.sagepub.com/page/policies/text-and-data-mining-license","oa_locations":[{"url":"https://europepmc.org/articles/pmc3998729?pdf=render","host_type":"GREEN"},{"url":"https://journals.sagepub.com/doi/pdf/10.1177/0962280211403597","host_type":"publisher"},{"url":"https://journals.sagepub.com/doi/full-xml/10.1177/0962280211403597","host_type":"publisher"},{"url":"https://doi.org/10.1177/0962280211403597","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/21705435","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3998729","host_type":"repository"},{"url":"https://hdl.handle.net/1805/49900","host_type":"repository"}],"fields_of_study":["Statistical Methods and Bayesian Inference","Statistical Methods and Inference","Bayesian Methods and Mixture Models","Medicine","Mathematics","Data Interpretation, Statistical","Models, Statistical","Probability"],"mesh_terms":["Data Interpretation, Statistical","Probability","Models, Statistical"],"keywords":["Missing data","Inverse probability weighting","Weighting","Computer science","Data mining","Imputation (statistics)","Population","Estimator","Machine learning","Statistics","Mathematics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"}],"linked_datasets":[{"doi":"10.6084/m9.figshare.19921013.v1","title":"Additional file 1 of Using observational study data as an external control group for a clinical trial: an empirical comparison of methods to account for longitudinal missing data","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.19921013","title":"Additional file 1 of Using observational study data as an external control group for a clinical trial: an empirical comparison of methods to account for longitudinal missing data","publisher":"figshare","resource_type":"JournalArticle"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-04T00:59:19.504638Z","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,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}