Efficient semiparametric inference for two-phase studies with outcome and covariate measurement errors.

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,

SUBMITTER: Tao R 

PROVIDER: S-EPMC8214478 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature