{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Oh EJ"],"funding":["NIAID NIH HHS","National Institutes of Health","Patient-Centered Outcomes Research Institute"],"pagination":["631-649"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7874496"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["40(3)"],"pubmed_abstract":["Medical studies that depend on electronic health records (EHR) data are often subject to measurement error, as the data are not collected to support research questions under study. These data errors, if not accounted for in study analyses, can obscure or cause spurious associations between patient exposures and disease risk. Methodology to address covariate measurement error has been well developed; however, time-to-event error has also been shown to cause significant bias, but methods to address it are relatively underdeveloped. More generally, it is possible to observe errors in both the covariate and the time-to-event outcome that are correlated. We propose regression calibration (RC) estimators to simultaneously address correlated error in the covariates and the censored event time. Although RC can perform well in many settings with covariate measurement error, it is biased for nonlinear regression models, such as the Cox model. Thus, we additionally propose raking estimators which are consistent estimators of the parameter defined by the population estimating equation. Raking can improve upon RC in certain settings with failure-time data, require no explicit modeling of the error structure, and can be utilized under outcome-dependent sampling designs. We discuss features of the underlying estimation problem that affect the degree of improvement the raking estimator has over the RC approach. Detailed simulation studies are presented to examine the performance of the proposed estimators under varying levels of signal, error, and censoring. The methodology is illustrated on observational EHR data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic."],"journal":["Statistics in medicine"],"pubmed_title":["Raking and regression calibration: Methods to address bias from correlated covariate and time-to-event error."],"pmcid":["PMC7874496"],"funding_grant_id":["U01 AI069923","R01 AI093234","R01‐AI131771","P30 AI110527","R37 AI131771","U01 AI069918","R01‐AI093234","U01‐AI069923","P30‐AI110527","R01 AI131771","U01‐AI069918","R‐1609‐36207"],"pubmed_authors":["Shepherd BE","Lumley T","Oh EJ","Shaw PA"],"additional_accession":[]},"is_claimable":false,"name":"Raking and regression calibration: Methods to address bias from correlated covariate and time-to-event error.","description":"Medical studies that depend on electronic health records (EHR) data are often subject to measurement error, as the data are not collected to support research questions under study. These data errors, if not accounted for in study analyses, can obscure or cause spurious associations between patient exposures and disease risk. Methodology to address covariate measurement error has been well developed; however, time-to-event error has also been shown to cause significant bias, but methods to address it are relatively underdeveloped. More generally, it is possible to observe errors in both the covariate and the time-to-event outcome that are correlated. We propose regression calibration (RC) estimators to simultaneously address correlated error in the covariates and the censored event time. Although RC can perform well in many settings with covariate measurement error, it is biased for nonlinear regression models, such as the Cox model. Thus, we additionally propose raking estimators which are consistent estimators of the parameter defined by the population estimating equation. Raking can improve upon RC in certain settings with failure-time data, require no explicit modeling of the error structure, and can be utilized under outcome-dependent sampling designs. We discuss features of the underlying estimation problem that affect the degree of improvement the raking estimator has over the RC approach. Detailed simulation studies are presented to examine the performance of the proposed estimators under varying levels of signal, error, and censoring. The methodology is illustrated on observational EHR data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Feb","modification":"2025-04-03T23:34:04.235Z","creation":"2025-04-03T23:34:04.235Z"},"accession":"S-EPMC7874496","cross_references":{"pubmed":["33140432"],"doi":["10.1002/sim.8793"]}}