{"doi":"10.1093/aje/kwy210","title":"Propensity Score–Based Estimators With Multiple Error-Prone Covariates","abstract":null,"journal":"American Journal of Epidemiology","year":2019,"id":594763,"datarank":0.38474240361923057,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.0,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"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":1522707,"name":"David A Aaby","orcid":null,"position":1,"is_corresponding":false},{"id":415851,"name":"Juned Siddique","orcid":"0000-0002-1501-4152","position":2,"is_corresponding":false},{"id":405833,"name":"Elizabeth A Stuart","orcid":null,"position":3,"is_corresponding":false},{"id":341409,"name":"Hwanhee Hong","orcid":"0000-0002-3736-6327","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Propensity Score–Based Estimators With Multiple Error-Prone Covariates","abstract":"Propensity score methods are an important tool to help reduce confounding in nonexperimental studies. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error, which leads to biased causal effect estimates if the true underlying covariates are the actual confounders. Although some groups have investigated the impact of a single mismeasured covariate on estimating a causal effect and proposed methods for handling the measurement error, fewer have investigated the case where multiple covariates are mismeasured, and we found none that discussed correlated measurement errors. In this study, we examined the consequences of multiple error-prone covariates when estimating causal effects using propensity score-based estimators via extensive simulation studies and real data analyses. We found that causal effect estimates are less biased when the propensity score model includes mismeasured covariates whose true underlying values are strongly correlated with each other. However, when the measurement errors are correlated with each other, additional bias is introduced. In addition, it is beneficial to include correctly measured auxiliary variables that are correlated with confounders whose true underlying values are mismeasured in the propensity score model.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"30358801","pmcid":"PMC6321809","openalex_id":null,"authors":[],"funders":[{"funder_name":"National Institute of Mental Health","grant_id":"R01MH099010","title":null},{"funder_name":"National Heart, Lung, and Blood Institute","grant_id":"R01HL127491","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01HL127491-03","title":"Statistical methods to correct for measurement error in self-reported dietary data from lifestyle intervention trials"},{"funder_name":"National Institutes of Health","grant_id":"4R01MH099010-04","title":"Using propensity scores for causal inference with covariate measurement error"}],"total_grants":4,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"bronze","license":"OUP Standard Publication Reuse","oa_locations":[{"url":"https://academic.oup.com/aje/article-pdf/188/1/222/27238855/kwy210.pdf","host_type":"publisher"},{"url":"http://academic.oup.com/aje/article-pdf/188/1/222/27238855/kwy210.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/aje/kwy210","host_type":""},{"url":"https://dx.doi.org/10.48550/arxiv.1706.02283","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/30358801","host_type":""},{"url":"http://arxiv.org/abs/1706.02283","host_type":""},{"url":"https://dx.doi.org/10.1093/aje/kwy210","host_type":""}],"fields_of_study":["0101 mathematics","01 natural sciences"],"mesh_terms":["Humans","Epidemiologic Methods","Data Interpretation, Statistical","Models, Statistical","Causality","Computer Simulation","Propensity Score","Bias"],"keywords":["Causality","Methodology (stat.ME)","FOS: Computer and information sciences","Models, Statistical","Bias","Data Interpretation, Statistical","Humans","Computer Simulation","Epidemiologic Methods","Propensity Score","Statistics - Methodology"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-27T15:29:56.776759Z","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":[]}