ArticleStatistics in medicine2021
Efficient semiparametric inference for two-phase studies with outcome and covariate measurement errors.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- 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
- Ascertainment Conditional Maximum Likelihood for Continuous Outcome Under Two-Phase Response-Selective Design.Statistics in medicine · 2025Article
- Should regression calibration or multiple imputation be used when calibrating different devices in a longitudinal study?American journal of epidemiology · 2025Article
- sleev: An R Package for Semiparametric Likelihood Estimation with Errors in Variables.Journal of open source software · 2025Article
- Applying survey weights to ordinal regression models for improved inference in outcome-dependent samples with ordinal outcomes.Statistical methods in medical research · 2024Article
- Study design features increase replicability in cross-sectional and longitudinal brain-wide association studies.bioRxiv : the preprint server for biology · 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
In modern observational studies using electronic health records or other routinely collected data, both the outcome and covariates of interest can be error-prone and their errors often correlated. A cost-effective solution is the two-phase design, under which the error-prone outcome and covariates are observed for all subjects during the first phase and that information is used to select a validation subsample for accurate measurements of these variables in the second phase. Previous research on two-phase measurement error problems largely focused on scenarios where there are errors in covariates only or the validation sample is a simple random sample of study subjects. Herein, we propose a semiparametric approach to general two-phase measurement error problems with a quantitative outcome, allowing for correlated errors in the outcome and covariates and arbitrary second-phase selection. We devise a computationally efficient and numerically stable expectation-maximization algorithm to maximize the nonparametric likelihood function. The resulting estimators possess desired statistical properties. We demonstrate the superiority of the proposed methods over existing approaches through extensive simulation studies, and we illustrate their use in an observational HIV study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.