4 citations · 4 across the 5 of their papers we have counts for
6 papers · 1 filter
Three-phase generalized raking and multiple imputation estimators to address error-prone data
Gustavo Amorim, Ran Tao, Sarah Lotspeich +4
Validation studies are often used to obtain more reliable information in settings with error-prone data. Validated data on a subsample of subjects can be used together with error-p…
Optimum Allocation for Adaptive Multi-Wave Sampling in R: The R Package optimall
Jasper B. Yang, Bryan E. Shepherd, Thomas Lumley +1
The R package optimall offers a collection of functions that efficiently streamline the design process of sampling in surveys ranging from simple to complex. The package's main fun…
Improved Generalized Raking Estimators to Address Dependent Covariate and Failure-Time Outcome Error
Eric J. Oh, Bryan E. Shepherd, Thomas Lumley +1
Biomedical studies that use electronic health records (EHR) data for inference are often subject to bias due to measurement error. The measurement error present in EHR data is typi…
Two-phase analysis and study design for survival models with error-prone exposures
Kyunghee Han, Thomas Lumley, Bryan E. Shepherd +1
Increasingly, medical research is dependent on data collected for non-research purposes, such as electronic health records data (EHR). EHR data and other large databases can be pro…
Raking and Regression Calibration: Methods to Address Bias from Correlated Covariate and Time-to-Event Error
Eric J. Oh, Bryan E. Shepherd, Thomas Lumley +1
Medical studies that depend on electronic health records (EHR) data are often subject to measurement error, as the data are not collected to support research questions under study.…
Regression calibration to correct correlated errors in outcome and exposure
Pamela Shaw, Jiwei He, Bryan Shepherd
Measurement error arises through a variety of mechanisms. A rich literature exists on the bias introduced by covariate measurement error and on methods of analysis to address this…