{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Amorim G"],"funding":["NIAID NIH HHS","National Institutes of Health","Patient Centered Outcome Research Institute"],"pagination":["1368-1389"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8715909"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["184(4)"],"pubmed_abstract":["Measurement errors are present in many data collection procedures and can harm analyses by biasing estimates. To correct for measurement error, researchers often validate a subsample of records and then incorporate the information learned from this validation sample into estimation. In practice, the validation sample is often selected using simple random sampling (SRS). However, SRS leads to inefficient estimates because it ignores information on the error-prone variables, which can be highly correlated to the unknown truth. Applying and extending ideas from the two-phase sampling literature, we propose optimal and nearly-optimal designs for selecting the validation sample in the classical measurement-error framework. We target designs to improve the efficiency of model-based and design-based estimators, and show how the resulting designs compare to each other. Our results suggest that sampling schemes that extract more information from the error-prone data are substantially more efficient than SRS, for both design- and model-based estimators. The optimal procedure, however, depends on the analysis method, and can differ substantially. This is supported by theory and simulations. We illustrate the various designs using data from an HIV cohort study."],"journal":["Journal of the Royal Statistical Society. Series A, (Statistics in Society)"],"pubmed_title":["Two-Phase Sampling Designs for Data Validation in Settings with Covariate Measurement Error and Continuous Outcome."],"pmcid":["PMC8715909"],"funding_grant_id":["R-1609-36207","P30 AI110527","R37 AI131771","U01 AI069918","R01 AI131771"],"pubmed_authors":["Shepherd BE","Lumley T","Tao R","Lotspeich S","Shaw PA","Amorim G"],"additional_accession":[]},"is_claimable":false,"name":"Two-Phase Sampling Designs for Data Validation in Settings with Covariate Measurement Error and Continuous Outcome.","description":"Measurement errors are present in many data collection procedures and can harm analyses by biasing estimates. To correct for measurement error, researchers often validate a subsample of records and then incorporate the information learned from this validation sample into estimation. In practice, the validation sample is often selected using simple random sampling (SRS). However, SRS leads to inefficient estimates because it ignores information on the error-prone variables, which can be highly correlated to the unknown truth. Applying and extending ideas from the two-phase sampling literature, we propose optimal and nearly-optimal designs for selecting the validation sample in the classical measurement-error framework. We target designs to improve the efficiency of model-based and design-based estimators, and show how the resulting designs compare to each other. Our results suggest that sampling schemes that extract more information from the error-prone data are substantially more efficient than SRS, for both design- and model-based estimators. The optimal procedure, however, depends on the analysis method, and can differ substantially. This is supported by theory and simulations. We illustrate the various designs using data from an HIV cohort study.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2026-06-04T04:34:23.228Z","creation":"2025-02-19T02:24:11.771Z"},"accession":"S-EPMC8715909","cross_references":{"pubmed":["34975235"],"doi":["10.1111/rssa.12689"]}}