activity
20112022
most citedModel-robust regression and a Bayesian ``sandwich'' estimator

42 citations · 49 across the 11 of their papers we have counts for

collaborators

14 papers

stat.ME2022

Practical considerations for sandwich variance estimation in two-stage regression settings

Lillian A. Boe, Thomas Lumley, Pamela A. Shaw

We present a practical approach for computing the sandwich variance estimator in two-stage regression model settings. As a motivating example for two-stage regression, we consider…

stat.ME2022

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…

stat.ME2022

Choosing good subsamples for regression modelling

Thomas Lumley, Tong Chen

A common problem in health research is that we have a large database with many variables measured on a large number of individuals. We are interested in measuring additional variab…

stat.AP2021

Analysis of Error-prone Electronic Health Records with Multi-wave Validation Sampling: Association of Maternal Weight Gain during Pregnancy with Childhood Outcomes

Bryan E. Shepherd, Kyunghee Han, Tong Chen +6

Electronic health record (EHR) data are increasingly used for biomedical research, but these data have recognized data quality challenges. Data validation is necessary to use EHR d…

stat.ME2021

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…

stat.ME2020

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…