ArticleJournal of the Royal Statistical Society. Series A, (Statistics in Society)2021
Two-Phase Sampling Designs for Data Validation in Settings with Covariate Measurement Error and Continuous Outcome.
Article in Journal of the Royal Statistical Society. Series A, (Statistics in Society), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- The Caribbean, Central and South America Network for HIV Epidemiology: 20 Years of HIV Research in the Region.Journal of the International AIDS Society · 2026Article
- Validation of a Risk-Prediction Model in the Presence of Outcome Misclassification.Statistics in medicine · 2026Article
- Adaptive Multi-Wave Sampling for Efficient Chart Validation.Clinical epidemiology · 2026Article
- Data validation in multinational observational studies with error-prone data: applying an optimal validation sampling strategy in a study of Kaposi sarcoma and HIV.American journal of epidemiology · 2025Article
- Flexible and efficient estimation of causal effects with error-prone exposures: a control variates approach for measurement error.Biometrics · 2025Article
- Integrating Misclassified EHR Outcomes With Validated Outcomes From a Non-Probability Sample.Statistics in medicine · 2025Article
- Combining Straight-Line and Map-Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease.Statistics in medicine · 2025Article
- Optimum Allocation for Adaptive Multi-Wave Sampling in R: The R Package optimall.Journal of statistical software · 2025Article
- Comparison of calibration methods in the analysis of 2013 Brazilian National Health Survey data.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2025Article
- Double Sampling for Informatively Missing Data in Electronic Health Record-Based Comparative Effectiveness Research.Statistics in medicine · 2024Article
- A SEMIPARAMETRIC METHOD FOR RISK PREDICTION USING INTEGRATED ELECTRONIC HEALTH RECORD DATA.The annals of applied statistics · 2024Article
- Optimal multiwave validation of secondary use data with outcome and exposure misclassification.The Canadian journal of statistics = Revue canadienne de statistique · 2024Article
- Three-phase generalized raking and multiple imputation estimators to address error-prone data.Statistics in medicine · 2024Article
- Article
- Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa.Journal of clinical and translational science · 2023Article
- Optimal sampling for design-based estimators of regression models.Statistics in medicine · 2022Article
- Errors in multiple variables in human immunodeficiency virus (HIV) cohort and electronic health record data: statistical challenges and opportunities.Statistical communications in infectious diseases · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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