ArticleBiometrics2022
Efficient odds ratio estimation under two-phase sampling using error-prone data from a multi-national HIV research cohort.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 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
- Association between documented mental health disorders and HIV treatment outcomes among adults receiving antiretroviral therapy in rural Zimbabwe.BioPsychoSocial medicine · 2026Article
- Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records.Journal of biomedical informatics · 2025Article
- Ascertainment Conditional Maximum Likelihood for Continuous Outcome Under Two-Phase Response-Selective Design.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
- sleev: An R Package for Semiparametric Likelihood Estimation with Errors in Variables.Journal of open source software · 2025Article
- Optimal multiwave validation of secondary use data with outcome and exposure misclassification.The Canadian journal of statistics = Revue canadienne de statistique · 2024Article
- Association analysis of self-reported outcomes with a validated subset.Statistics in medicine · 2024Article
- 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
- 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
5 authors.
Funding
Abstract
Persons living with HIV engage in routine clinical care, generating large amounts of data in observational HIV cohorts. These data are often error-prone, and directly using them in biomedical research could bias estimation and give misleading results. A cost-effective solution is the two-phase design, under which the error-prone variables are observed for all patients during Phase I, and that information is used to select patients for data auditing during Phase II. For example, the Caribbean, Central, and South America network for HIV epidemiology (CCASAnet) selected a random sample from each site for data auditing. Herein, we consider efficient odds ratio estimation with partially audited, error-prone data. We propose a semiparametric approach that uses all information from both phases and accommodates a number of error mechanisms. We allow both the outcome and covariates to be error-prone and these errors to be correlated, and selection of the Phase II sample can depend on Phase I data in an arbitrary manner. We devise a computationally efficient, numerically stable EM algorithm to obtain estimators that are consistent, asymptotically normal, and asymptotically efficient. We demonstrate the advantages of the proposed methods over existing ones through extensive simulations. Finally, we provide applications to the CCASAnet cohort.
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