3 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
Debiased Machine Learning for Count Data: a Partially Linear Poisson Model Based on Neural Networks
Ningkun Zhou, Bryan E. Shepherd, Qingyan Xiang
We develop a novel debiased machine learning (DML) estimator to analyze count data, which are common in clinical or biomedical studies. Specifically, we estimate the effect of a bi…
Marginal generalized raking with parametric working models
Brian D Williamson, Runjia Zou, Thomas Lumley +2
Generalized raking (GR) was originally developed in the survey statistics literature to incorporate auxiliary information in estimation. Recently, it has been used in the biostatis…
Addressing errors in multiple variables using generalized raking and cumulative probability models
Eric S. Kawaguchi, Chun Li, Frank E. Harrell +3
Routinely collected data, such as electronic health record (EHR) data, are frequently used for biomedical research, but these data are prone to errors, which can bias study finding…
Improving optimal subsampling through stratification
Jasper B. Yang, Thomas Lumley, Bryan E. Shepherd +1
Recent works have proposed optimal subsampling algorithms to improve computational efficiency in large datasets and to design validation studies in the presence of measurement erro…
Assessing treatment effects in observational data with missing confounders: A comparative study of practical doubly-robust and traditional missing data methods
Brian D. Williamson, Chloe Krakauer, Eric Johnson +13
In pharmacoepidemiology, safety and effectiveness are frequently evaluated using readily available administrative and electronic health records data. In these settings, detailed co…